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• Published: 02 May 2024

Effectiveness of social media-assisted course on learning self-efficacy

• Jiaying Hu 1 ,
• Yicheng Lai 2 &
• Xiuhua Yi 3  

Scientific Reports volume  14 , Article number:  10112 ( 2024 ) Cite this article

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• Human behaviour

The social media platform and the information dissemination revolution have changed the thinking, needs, and methods of students, bringing development opportunities and challenges to higher education. This paper introduces social media into the classroom and uses quantitative analysis to investigate the relation between design college students’ learning self-efficacy and social media for design students, aiming to determine the effectiveness of social media platforms on self-efficacy. This study is conducted on university students in design media courses and is quasi-experimental, using a randomized pre-test and post-test control group design. The study participants are 73 second-year design undergraduates. Independent samples t-tests showed that the network interaction factors of social media had a significant impact on college students learning self-efficacy. The use of social media has a significant positive predictive effect on all dimensions of learning self-efficacy. Our analysis suggests that using the advantages and value of online social platforms, weakening the disadvantages of the network, scientifically using online learning resources, and combining traditional classrooms with the Internet can improve students' learning self-efficacy.

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Introduction.

Social media is a way of sharing information, ideas, and opinions with others one. It can be used to create relationships between people and businesses. Social media has changed the communication way, it’s no longer just about talking face to face but also using a digital platform such as Facebook or Twitter. Today, social media is becoming increasingly popular in everyone's lives, including students and researchers 1 . Social media provides many opportunities for learners to publish their work globally, bringing many benefits to teaching and learning. The publication of students' work online has led to a more positive attitude towards learning and increased achievement and motivation. Other studies report that student online publications or work promote reflection on personal growth and development and provide opportunities for students to imagine more clearly the purpose of their work 2 . In addition, learning environments that include student publications allow students to examine issues differently, create new connections, and ultimately form new entities that can be shared globally 3 , 4 .

Learning self-efficacy is a belief that you can learn something new. It comes from the Latin word “self” and “efficax” which means efficient or effective. Self-efficacy is based on your beliefs about yourself, how capable you are to learn something new, and your ability to use what you have learned in real-life situations. This concept was first introduced by Bandura (1977), who studied the effects of social reinforcement on children’s learning behavior. He found that when children were rewarded for their efforts they would persist longer at tasks that they did not like or had low interest in doing. Social media, a ubiquitous force in today's digital age, has revolutionized the way people interact and share information. With the rise of social media platforms, individuals now have access to a wealth of online resources that can enhance their learning capabilities. This access to information and communication has also reshaped the way students approach their studies, potentially impacting their learning self-efficacy. Understanding the role of social media in shaping students' learning self-efficacy is crucial in providing effective educational strategies that promote healthy learning and development 5 . Unfortunately, the learning curve for the associated metadata base modeling methodologies and their corresponding computer-aided software engineering (CASE) tools have made it difficult for students to grasp. Addressing this learning issue examined the effect of this MLS on the self-efficacy of learning these topics 6 . Bates et al. 7 hypothesize a mediated model in which a set of antecedent variables influenced students’ online learning self-efficacy which, in turn, affected student outcome expectations, mastery perceptions, and the hours spent per week using online learning technology to complete learning assignments for university courses. Shen et al. 8 through exploratory factor analysis identifies five dimensions of online learning self-efficacy: (a) self-efficacy to complete an online course (b) self-efficacy to interact socially with classmates (c) self-efficacy to handle tools in a Course Management System (CMS) (d) self-efficacy to interact with instructors in an online course, and (e) self-efficacy to interact with classmates for academic purposes. Chiu 9 established a model for analyzing the mediating effect that learning self-efficacy and social self-efficacy have on the relationship between university students’ perceived life stress and smartphone addiction. Kim et al. 10 study was conducted to examine the influence of learning efficacy on nursing students' self-confidence. The objective of Paciello et al. 11 was to identify self-efficacy configurations in different domains (i.e., emotional, social, and self-regulated learning) in a sample of university students using a person-centered approach. The role of university students’ various conceptions of learning in their academic self-efficacy in the domain of physics is initially explored 12 . Kumar et al. 13 investigated factors predicting students’ behavioral intentions towards the continuous use of mobile learning. Other influential work includes 14 .

Many studies have focused on social networking tools such as Facebook and MySpace 15 , 16 . Teachers are concerned that the setup and use of social media apps take up too much of their time, may have plagiarism and privacy issues, and contribute little to actual student learning outcomes; they often consider them redundant or simply not conducive to better learning outcomes 17 . Cao et al. 18 proposed that the central questions in addressing the positive and negative pitfalls of social media on teaching and learning are whether the use of social media in teaching and learning enhances educational effectiveness, and what motivates university teachers to use social media in teaching and learning. Maloney et al. 3 argued that social media can further improve the higher education teaching and learning environment, where students no longer access social media to access course information. Many studies in the past have shown that the use of modern IT in the classroom has increased over the past few years; however, it is still limited mainly to content-driven use, such as accessing course materials, so with the emergence of social media in students’ everyday lives 2 , we need to focus on developing students’ learning self-efficacy so that they can This will enable students to 'turn the tables and learn to learn on their own. Learning self-efficacy is considered an important concept that has a powerful impact on learning outcomes 19 , 20 .

Self-efficacy for learning is vital in teaching students to learn and develop healthily and increasing students' beliefs in the learning process 21 . However, previous studies on social media platforms such as Twitter and Weibo as curriculum support tools have not been further substantiated or analyzed in detail. In addition, the relationship between social media, higher education, and learning self-efficacy has not yet been fully explored by researchers in China. Our research aims to fill this gap in the topic. Our study explored the impact of social media on the learning self-efficacy of Chinese college students. Therefore, it is essential to explore the impact of teachers' use of social media to support teaching and learning on students' learning self-efficacy. Based on educational theory and methodological practice, this study designed a teaching experiment using social media to promote learning self-efficacy by posting an assignment for post-course work on online media to explore the actual impact of social media on university students’ learning self-efficacy. This study examines the impact of a social media-assisted course on university students' learning self-efficacy to explore the positive impact of a social media-assisted course.

Theoretical background

• Social media

Social media has different definitions. Mayfield (2013) first introduced the concept of social media in his book-what is social media? The author summarized the six characteristics of social media: openness, participation, dialogue, communication, interaction, and communication. Mayfield 22 shows that social media is a kind of new media. Its uniqueness is that it can give users great space and freedom to participate in the communication process. Jen (2020) also suggested that the distinguishing feature of social media is that it is “aggregated”. Social media provides users with an interactive service to control their data and information and collaborate and share information 2 . Social media offers opportunities for students to build knowledge and helps them actively create and share information 23 . Millennial students are entering higher education institutions and are accustomed to accessing and using data from the Internet. These individuals go online daily for educational or recreational purposes. Social media is becoming increasingly popular in the lives of everyone, including students and researchers 1 . A previous study has shown that millennials use the Internet as their first source of information and Google as their first choice for finding educational and personal information 24 . Similarly, many institutions encourage teachers to adopt social media applications 25 . Faculty members have also embraced social media applications for personal, professional, and pedagogical purposes 17 .

Social networks allow one to create a personal profile and build various networks that connect him/her to family, friends, and other colleagues. Users use these sites to stay in touch with their friends, make plans, make new friends, or connect with someone online. Therefore, extending this concept, these sites can establish academic connections or promote cooperation and collaboration in higher education classrooms 2 . This study defines social media as an interactive community of users' information sharing and social activities built on the technology of the Internet. Because the concept of social media is broad, its connotations are consistent. Research shows that Meaning and Linking are the two key elements that make up social media existence. Users and individual media outlets generate social media content and use it as a platform to get it out there. Social media distribution is based on social relationships and has a better platform for personal information and relationship management systems. Examples of social media applications include Facebook, Twitter, MySpace, YouTube, Flickr, Skype, Wiki, blogs, Delicious, Second Life, open online course sites, SMS, online games, mobile applications, and more 18 . Ajjan and Hartshorne 2 investigated the intentions of 136 faculty members at a US university to adopt Web 2.0 technologies as tools in their courses. They found that integrating Web 2.0 technologies into the classroom learning environment effectively increased student satisfaction with the course and improved their learning and writing skills. His research focused on improving the perceived usefulness, ease of use, compatibility of Web 2.0 applications, and instructor self-efficacy. The social computing impact of formal education and training and informal learning communities suggested that learning web 2.0 helps users to acquire critical competencies, and promotes technological, pedagogical, and organizational innovation, arguing that social media has a variety of learning content 26 . Users can post digital content online, enabling learners to tap into tacit knowledge while supporting collaboration between learners and teachers. Cao and Hong 27 investigated the antecedents and consequences of social media use in teaching among 249 full-time and part-time faculty members, who reported that the factors for using social media in teaching included personal social media engagement and readiness, external pressures; expected benefits; and perceived risks. The types of Innovators, Early adopters, Early majority, Late majority, Laggards, and objectors. Cao et al. 18 studied the educational effectiveness of 168 teachers' use of social media in university teaching. Their findings suggest that social media use has a positive impact on student learning outcomes and satisfaction. Their research model provides educators with ideas on using social media in the education classroom to improve student performance. Maqableh et al. 28 investigated the use of social networking sites by 366 undergraduate students, and they found that weekly use of social networking sites had a significant impact on student's academic performance and that using social networking sites had a significant impact on improving students' effective time management, and awareness of multitasking. All of the above studies indicate the researcher’s research on social media aids in teaching and learning. All of these studies indicate the positive impact of social media on teaching and learning.

• Learning self-efficacy

For the definition of concepts related to learning self-efficacy, scholars have mainly drawn on the idea proposed by Bandura 29 that defines self-efficacy as “the degree to which people feel confident in their ability to use the skills they possess to perform a task”. Self-efficacy is an assessment of a learner’s confidence in his or her ability to use the skills he or she possesses to complete a learning task and is a subjective judgment and feeling about the individual’s ability to control his or her learning behavior and performance 30 . Liu 31 has defined self-efficacy as the belief’s individuals hold about their motivation to act, cognitive ability, and ability to perform to achieve their goals, showing the individual's evaluation and judgment of their abilities. Zhang (2015) showed that learning efficacy is regarded as the degree of belief and confidence that expresses the success of learning. Yan 32 showed the extent to which learning self-efficacy is viewed as an individual. Pan 33 suggested that learning self-efficacy in an online learning environment is a belief that reflects the learner's ability to succeed in the online learning process. Kang 34 believed that learning self-efficacy is the learner's confidence and belief in his or her ability to complete a learning task. Huang 35 considered self-efficacy as an individual’s self-assessment of his or her ability to complete a particular task or perform a specific behavior and the degree of confidence in one’s ability to achieve a specific goal. Kong 36 defined learning self-efficacy as an individual’s judgment of one’s ability to complete academic tasks.

Based on the above analysis, we found that scholars' focus on learning self-efficacy is on learning behavioral efficacy and learning ability efficacy, so this study divides learning self-efficacy into learning behavioral efficacy and learning ability efficacy for further analysis and research 37 , 38 . Search the CNKI database and ProQuest Dissertations for keywords such as “design students’ learning self-efficacy”, “design classroom self-efficacy”, “design learning self-efficacy”, and other keywords. There are few relevant pieces of literature about design majors. Qiu 39 showed that mobile learning-assisted classroom teaching can control the source of self-efficacy from many aspects, thereby improving students’ sense of learning efficacy and helping middle and lower-level students improve their sense of learning efficacy from all dimensions. Yin and Xu 40 argued that the three elements of the network environment—“learning content”, “learning support”, and “social structure of learning”—all have an impact on university students’ learning self-efficacy. Duo et al. 41 recommend that learning activities based on the mobile network learning community increase the trust between students and the sense of belonging in the learning community, promote mutual communication and collaboration between students, and encourage each other to stimulate their learning motivation. In the context of social media applications, self-efficacy refers to the level of confidence that teachers can successfully use social media applications in the classroom 18 . Researchers have found that self-efficacy is related to social media applications 42 . Students had positive experiences with social media applications through content enhancement, creativity experiences, connectivity enrichment, and collaborative engagement 26 . Students who wish to communicate with their tutors in real-time find social media tools such as web pages, blogs, and virtual interactions very satisfying 27 . Overall, students report their enjoyment of different learning processes through social media applications; simultaneously, they show satisfactory tangible achievement of tangible learning outcomes 18 . According to Bandura's 'triadic interaction theory’, Bian 43 and Shi 44 divided learning self-efficacy into two main elements, basic competence, and control, where basic competence includes the individual's sense of effort, competence, the individual sense of the environment, and the individual's sense of control over behavior. The primary sense of competence includes the individual's Sense of effort, competence, environment, and control over behavior. In this study, learning self-efficacy is divided into Learning behavioral efficacy and Learning ability efficacy. Learning behavioral efficacy includes individuals' sense of effort, environment, and control; learning ability efficacy includes individuals' sense of ability, belief, and interest.

In Fig.  1 , learning self-efficacy includes learning behavior efficacy and learning ability efficacy, in which the learning behavior efficacy is determined by the sense of effort, the sense of environment, the sense of control, and the learning ability efficacy is determined by the sense of ability, sense of belief, sense of interest. “Sense of effort” is the understanding of whether one can study hard. Self-efficacy includes the estimation of self-effort and the ability, adaptability, and creativity shown in a particular situation. One with a strong sense of learning self-efficacy thinks they can study hard and focus on tasks 44 . “Sense of environment” refers to the individual’s feeling of their learning environment and grasp of the environment. The individual is the creator of the environment. A person’s feeling and grasp of the environment reflect the strength of his sense of efficacy to some extent. A person with a shared sense of learning self-efficacy is often dissatisfied with his environment, but he cannot do anything about it. He thinks the environment can only dominate him. A person with a high sense of learning self-efficacy will be more satisfied with his school and think that his teachers like him and are willing to study in school 44 . “Sense of control” is an individual’s sense of control over learning activities and learning behavior. It includes the arrangement of individual learning time, whether they can control themselves from external interference, and so on. A person with a strong sense of self-efficacy will feel that he is the master of action and can control the behavior and results of learning. Such a person actively participates in various learning activities. When he encounters difficulties in learning, he thinks he can find a way to solve them, is not easy to be disturbed by the outside world, and can arrange his own learning time. The opposite is the sense of losing control of learning behavior 44 . “Sense of ability” includes an individual’s perception of their natural abilities, expectations of learning outcomes, and perception of achieving their learning goals. A person with a high sense of learning self-efficacy will believe that he or she is brighter and more capable in all areas of learning; that he or she is more confident in learning in all subjects. In contrast, people with low learning self-efficacy have a sense of powerlessness. They are self-doubters who often feel overwhelmed by their learning and are less confident that they can achieve the appropriate learning goals 44 . “Sense of belief” is when an individual knows why he or she is doing something, knows where he or she is going to learn, and does not think before he or she even does it: What if I fail? These are meaningless, useless questions. A person with a high sense of learning self-efficacy is more robust, less afraid of difficulties, and more likely to reach their learning goals. A person with a shared sense of learning self-efficacy, on the other hand, is always going with the flow and is uncertain about the outcome of their learning, causing them to fall behind. “Sense of interest” is a person's tendency to recognize and study the psychological characteristics of acquiring specific knowledge. It is an internal force that can promote people's knowledge and learning. It refers to a person's positive cognitive tendency and emotional state of learning. A person with a high sense of self-efficacy in learning will continue to concentrate on studying and studying, thereby improving learning. However, one with low learning self-efficacy will have psychology such as not being proactive about learning, lacking passion for learning, and being impatient with learning. The elements of learning self-efficacy can be quantified and detailed in the following Fig.  1 .

Learning self-efficacy research structure in this paper.

Research participants

All the procedures were conducted in adherence to the guidelines and regulations set by the institution. Prior to initiating the study, informed consent was obtained in writing from the participants, and the Institutional Review Board for Behavioral and Human Movement Sciences at Nanning Normal University granted approval for all protocols.

Two parallel classes are pre-selected as experimental subjects in our study, one as the experimental group and one as the control group. Social media assisted classroom teaching to intervene in the experimental group, while the control group did not intervene. When selecting the sample, it is essential to consider, as far as possible, the shortcomings of not using randomization to select or assign the study participants, resulting in unequal experimental and control groups. When selecting the experimental subjects, classes with no significant differences in initial status and external conditions, i.e. groups with homogeneity, should be selected. Our study finally decided to select a total of 44 students from Class 2021 Design 1 and a total of 29 students from Class 2021 Design 2, a total of 74 students from Nanning Normal University, as the experimental subjects. The former served as the experimental group, and the latter served as the control group. 73 questionnaires are distributed to measure before the experiment, and 68 are returned, with a return rate of 93.15%. According to the statistics, there were 8 male students and 34 female students in the experimental group, making a total of 44 students (mirrors the demographic trends within the humanities and arts disciplines from which our sample was drawn); there are 10 male students and 16 female students in the control group, making a total of 26 students, making a total of 68 students in both groups. The sample of those who took the course were mainly sophomores, with a small number of first-year students and juniors, which may be related to the nature of the subject of this course and the course system offered by the university. From the analysis of students' majors, liberal arts students in the experimental group accounted for the majority, science students and art students accounted for a small part. In contrast, the control group had more art students, and liberal arts students and science students were small. In the daily self-study time, the experimental and control groups are 2–3 h. The demographic information of research participants is shown in Table 1 .

Research procedure

Firstly, the ADDIE model is used for the innovative design of the teaching method of the course. The number of students in the experimental group was 44, 8 male and 35 females; the number of students in the control group was 29, 10 male and 19 females. Secondly, the classes are targeted at students and applied. Thirdly, the course for both the experimental and control classes is a convenient and practice-oriented course, with the course title “Graphic Design and Production”, which focuses on learning the graphic design software Photoshop. The course uses different cases to explain in detail the process and techniques used to produce these cases using Photoshop, and incorporates practical experience as well as relevant knowledge in the process, striving to achieve precise and accurate operational steps; at the end of the class, the teacher assigns online assignments to be completed on social media, allowing students to post their edited software tutorials online so that students can master the software functions. The teacher assigns online assignments to be completed on social media at the end of the lesson, allowing students to post their editing software tutorials online so that they can master the software functions and production skills, inspire design inspiration, develop design ideas and improve their design skills, and improve students' learning self-efficacy through group collaboration and online interaction. Fourthly, pre-tests and post-tests are conducted in the experimental and control classes before the experiment. Fifthly, experimental data are collected, analyzed, and summarized.

We use a questionnaire survey to collect data. Self-efficacy is a person’s subjective judgment on whether one can successfully perform a particular achievement. American psychologist Albert Bandura first proposed it. To understand the improvement effect of students’ self-efficacy after the experimental intervention, this work questionnaire was referenced by the author from “Self-efficacy” “General Perceived Self Efficacy Scale” (General Perceived Self Efficacy Scale) German psychologist Schwarzer and Jerusalem (1995) and “Academic Self-Efficacy Questionnaire”, a well-known Chinese scholar Liang 45 .  The questionnaire content is detailed in the supplementary information . A pre-survey of the questionnaire is conducted here. The second-year students of design majors collected 32 questionnaires, eliminated similar questions based on the data, and compiled them into a formal survey scale. The scale consists of 54 items, 4 questions about basic personal information, and 50 questions about learning self-efficacy. The Likert five-point scale is the questionnaire used in this study. The answers are divided into “completely inconsistent", “relatively inconsistent”, “unsure”, and “relatively consistent”. The five options of “Completely Meet” and “Compliant” will count as 1, 2, 3, 4, and 5 points, respectively. Divided into a sense of ability (Q5–Q14), a sense of effort (Q15–Q20), a sense of environment (Q21–Q28), a sense of control (Q29–Q36), a sense of Interest (Q37–Q45), a sense of belief (Q46–Q54). To demonstrate the scientific effectiveness of the experiment, and to further control the influence of confounding factors on the experimental intervention. This article thus sets up a control group as a reference. Through the pre-test and post-test in different periods, comparison of experimental data through pre-and post-tests to illustrate the effects of the intervention.

Reliability indicates the consistency of the results of a measurement scale (See Table 2 ). It consists of intrinsic and extrinsic reliability, of which intrinsic reliability is essential. Using an internal consistency reliability test scale, a Cronbach's alpha coefficient of reliability statistics greater than or equal to 0.9 indicates that the scale has good reliability, 0.8–0.9 indicates good reliability, 7–0.8 items are acceptable. Less than 0.7 means to discard some items in the scale 46 . This study conducted a reliability analysis on the effects of the related 6-dimensional pre-test survey to illustrate the reliability of the questionnaire.

From the Table 2 , the Cronbach alpha coefficients for the pre-test, sense of effort, sense of environment, sense of control, sense of interest, sense of belief, and the total questionnaire, were 0.919, 0.839, 0.848, 0.865, 0.852, 0.889 and 0.958 respectively. The post-test Cronbach alpha coefficients were 0.898, 0.888, 0.886, 0.889, 0.900, 0.893 and 0.970 respectively. The Cronbach alpha coefficients were all greater than 0.8, indicating a high degree of reliability of the measurement data.

The validity, also known as accuracy, reflects how close the measurement result is to the “true value”. Validity includes structure validity, content validity, convergent validity, and discriminative validity. Because the experiment is a small sample study, we cannot do any specific factorization. KMO and Bartlett sphericity test values are an important part of structural validity. Indicator, general validity evaluation (KMO value above 0.9, indicating very good validity; 0.8–0.9, indicating good validity; 0.7–0.8 validity is good; 0.6–0.7 validity is acceptable; 0.5–0.6 means poor validity; below 0.45 means that some items should be abandoned.

Table 3 shows that the KMO values of ability, effort, environment, control, interest, belief, and the total questionnaire are 0.911, 0.812, 0.778, 0.825, 0.779, 0.850, 0.613, and the KMO values of the post-test are respectively. The KMO values are 0.887, 0.775, 0.892, 0.868, 0.862, 0.883, 0.715. KMO values are basically above 0.8, and all are greater than 0.6. This result indicates that the validity is acceptable, the scale has a high degree of reasonableness, and the valid data.

In the graphic design and production (professional design course), we will learn the practical software with cases. After class, we will share knowledge on the self-media platform. We will give face-to-face computer instruction offline from 8:00 to 11:20 every Wednesday morning for 16 weeks. China's top online sharing platform (APP) is Tik Tok, micro-blog (Micro Blog) and Xiao hong shu. The experiment began on September 1, 2022, and conducted the pre-questionnaire survey simultaneously. At the end of the course, on January 6, 2023, the post questionnaire survey was conducted. A total of 74 questionnaires were distributed in this study, recovered 74 questionnaires. After excluding the invalid questionnaires with incomplete filling and wrong answers, 68 valid questionnaires were obtained, with an effective rate of 91%, meeting the test requirements. Then, use the social science analysis software SPSS Statistics 26 to analyze the data: (1) descriptive statistical analysis of the dimensions of learning self-efficacy; (2) Using correlation test to analyze the correlation between learning self-efficacy and the use of social media; (3) This study used a comparative analysis of group differences to detect the influence of learning self-efficacy on various dimensions of social media and design courses. For data processing and analysis, use the spss26 version software and frequency statistics to create statistics on the basic situation of the research object and the basic situation of the use of live broadcast. The reliability scale analysis (internal consistency test) and use Bartlett's sphericity test to illustrate the reliability and validity of the questionnaire and the individual differences between the control group and the experimental group in demographic variables (gender, grade, Major, self-study time per day) are explained by cross-analysis (chi-square test). In the experimental group and the control group, the pre-test, post-test, before-and-after test of the experimental group and the control group adopt independent sample T-test and paired sample T-test to illustrate the effect of the experimental intervention (The significance level of the test is 0.05 two-sided).

Results and discussion

Comparison of pre-test and post-test between groups.

To study whether the data of the experimental group and the control group are significantly different in the pre-test and post-test mean of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. The research for this situation uses an independent sample T-test and an independent sample. The test needs to meet some false parameters, such as normality requirements. Generally passing the normality test index requirements are relatively strict, so it can be relaxed to obey an approximately normal distribution. If there is serious skewness distribution, replace it with the nonparametric test. Variables are required to be continuous variables. The six variables in this study define continuous variables. The variable value information is independent of each other. Therefore, we use the independent sample T-test.

From the Table 4 , a pre-test found that there was no statistically significant difference between the experimental group and the control group at the 0.05 confidence level ( p  > 0.05) for perceptions of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. Before the experiment, the two groups of test groups have the same quality in measuring self-efficacy. The experimental class and the control class are homogeneous groups. Table 5 shows the independent samples t-test for the post-test, used to compare the experimental and control groups on six items, including the sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief.

The experimental and control groups have statistically significant scores ( p  < 0.05) for sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief, and the experimental and control groups have statistically significant scores (t = 3.177, p  = 0.002) for a sense of competence. (t = 3.177, p  = 0.002) at the 0.01 level, with the experimental group scoring significantly higher (3.91 ± 0.51) than the control group (3.43 ± 0.73). The experimental group and the control group showed significance for the perception of effort at the 0.01 confidence level (t = 2.911, p  = 0.005), with the experimental group scoring significantly higher (3.88 ± 0.66) than the control group scoring significantly higher (3.31 ± 0.94). The experimental and control groups show significance at the 0.05 level (t = 2.451, p  = 0.017) for the sense of environment, with the experimental group scoring significantly higher (3.95 ± 0.61) than the control group scoring significantly higher (3.58 ± 0.62). The experimental and control groups showed significance for sense of control at the 0.05 level of significance (t = 2.524, p  = 0.014), and the score for the experimental group (3.76 ± 0.67) would be significantly higher than the score for the control group (3.31 ± 0.78). The experimental and control groups showed significance at the 0.01 level for sense of interest (t = 2.842, p  = 0.006), and the experimental group's score (3.87 ± 0.61) would be significantly higher than the control group's score (3.39 ± 0.77). The experimental and control groups showed significance at the 0.01 level for the sense of belief (t = 3.377, p  = 0.001), and the experimental group would have scored significantly higher (4.04 ± 0.52) than the control group (3.56 ± 0.65). Therefore, we can conclude that the experimental group's post-test significantly affects the mean scores of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. A social media-assisted course has a positive impact on students' self-efficacy.

Comparison of pre-test and post-test of each group

The paired-sample T-test is an extension of the single-sample T-test. The purpose is to explore whether the means of related (paired) groups are significantly different. There are four standard paired designs: (1) Before and after treatment of the same subject Data, (2) Data from two different parts of the same subject, (3) Test results of the same sample with two methods or instruments, 4. Two matched subjects receive two treatments, respectively. This study belongs to the first type, the 6 learning self-efficacy dimensions of the experimental group and the control group is measured before and after different periods.

Paired t-tests is used to analyze whether there is a significant improvement in the learning self-efficacy dimension in the experimental group after the experimental social media-assisted course intervention. In Table 6 , we can see that the six paired data groups showed significant differences ( p  < 0.05) in the pre and post-tests of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. There is a level of significance of 0.01 (t = − 4.540, p  = 0.000 < 0.05) before and after the sense of ability, the score after the sense of ability (3.91 ± 0.51), and the score before the Sense of ability (3.41 ± 0.55). The level of significance between the pre-test and post-test of sense of effort is 0.01 (t = − 4.002, p  = 0.000). The score of the sense of effort post-test (3.88 ± 0.66) will be significantly higher than the average score of the sense of effort pre-test (3.31 ± 0.659). The significance level between the pre-test and post-test Sense of environment is 0.01 (t = − 3.897, p  = 0.000). The average score for post- Sense of environment (3.95 ± 0.61) will be significantly higher than that of sense of environment—the average score of the previous test (3.47 ± 0.44). The average value of a post- sense of control (3.76 ± 0.67) will be significantly higher than the average of the front side of the Sense of control value (3.27 ± 0.52). The sense of interest pre-test and post-test showed a significance level of 0.01 (− 4.765, p  = 0.000), and the average value of Sense of interest post-test was 3.87 ± 0.61. It would be significantly higher than the average value of the Sense of interest (3.25 ± 0.59), the significance between the pre-test and post-test of belief sensing is 0.01 level (t = − 3.939, p  = 0.000). Thus, the average value of a post-sense of belief (4.04 ± 0.52) will be significantly higher than that of a pre-sense of belief Average value (3.58 ± 0.58). After the experimental group’s post-test, the scores for the Sense of ability, effort, environment, control, interest, and belief before the comparison experiment increased significantly. This result has a significant improvement effect. Table 7 shows that the control group did not show any differences in the pre and post-tests using paired t-tests on the dimensions of learning self-efficacy such as sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief ( p  > 0.05). It shows no experimental intervention for the control group, and it does not produce a significant effect.

The purpose of this study aims to explore the impact of social media use on college students' learning self-efficacy, examine the changes in the elements of college students' learning self-efficacy before and after the experiment, and make an empirical study to enrich the theory. This study developed an innovative design for course teaching methods using the ADDIE model. The design process followed a series of model rules of analysis, design, development, implementation, and evaluation, as well as conducted a descriptive statistical analysis of the learning self-efficacy of design undergraduates. Using questionnaires and data analysis, the correlation between the various dimensions of learning self-efficacy is tested. We also examined the correlation between the two factors, and verifies whether there was a causal relationship between the two factors.

Based on prior research and the results of existing practice, a learning self-efficacy is developed for university students and tested its reliability and validity. The scale is used to pre-test the self-efficacy levels of the two subjects before the experiment, and a post-test of the self-efficacy of the two groups is conducted. By measuring and investigating the learning self-efficacy of the study participants before the experiment, this study determined that there was no significant difference between the experimental group and the control group in terms of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. Before the experiment, the two test groups had homogeneity in measuring the dimensionality of learning self-efficacy. During the experiment, this study intervened in social media assignments for the experimental group. The experiment used learning methods such as network assignments, mutual aid communication, mutual evaluation of assignments, and group discussions. After the experiment, the data analysis showed an increase in learning self-efficacy in the experimental group compared to the pre-test. With the test time increased, the learning self-efficacy level of the control group decreased slightly. It shows that social media can promote learning self-efficacy to a certain extent. This conclusion is similar to Cao et al. 18 , who suggested that social media would improve educational outcomes.

We have examined the differences between the experimental and control group post-tests on six items, including the sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. This result proves that a social media-assisted course has a positive impact on students' learning self-efficacy. Compared with the control group, students in the experimental group had a higher interest in their major. They showed that they liked to share their learning experiences and solve difficulties in their studies after class. They had higher motivation and self-directed learning ability after class than students in the control group. In terms of a sense of environment, students in the experimental group were more willing to share their learning with others, speak boldly, and participate in the environment than students in the control group.

The experimental results of this study showed that the experimental group showed significant improvement in the learning self-efficacy dimensions after the experimental intervention in the social media-assisted classroom, with significant increases in the sense of ability, sense of effort, sense of environment, sense of control, sense of interest and sense of belief compared to the pre-experimental scores. This result had a significant improvement effect. Evidence that a social media-assisted course has a positive impact on students' learning self-efficacy. Most of the students recognized the impact of social media on their learning self-efficacy, such as encouragement from peers, help from teachers, attention from online friends, and recognition of their achievements, so that they can gain a sense of achievement that they do not have in the classroom, which stimulates their positive perception of learning and is more conducive to the awakening of positive effects. This phenomenon is in line with Ajjan and Hartshorne 2 . They argue that social media provides many opportunities for learners to publish their work globally, which brings many benefits to teaching and learning. The publication of students' works online led to similar positive attitudes towards learning and improved grades and motivation. This study also found that students in the experimental group in the post-test controlled their behavior, became more interested in learning, became more purposeful, had more faith in their learning abilities, and believed that their efforts would be rewarded. This result is also in line with Ajjan and Hartshorne's (2008) indication that integrating Web 2.0 technologies into classroom learning environments can effectively increase students' satisfaction with the course and improve their learning and writing skills.

We only selected students from one university to conduct a survey, and the survey subjects were self-selected. Therefore, the external validity and generalizability of our study may be limited. Despite the limitations, we believe this study has important implications for researchers and educators. The use of social media is the focus of many studies that aim to assess the impact and potential of social media in learning and teaching environments. We hope that this study will help lay the groundwork for future research on the outcomes of social media utilization. In addition, future research should further examine university support in encouraging teachers to begin using social media and university classrooms in supporting social media (supplementary file 1 ).

The present study has provided preliminary evidence on the positive association between social media integration in education and increased learning self-efficacy among college students. However, several avenues for future research can be identified to extend our understanding of this relationship.

Firstly, replication studies with larger and more diverse samples are needed to validate our findings across different educational contexts and cultural backgrounds. This would enhance the generalizability of our results and provide a more robust foundation for the use of social media in teaching. Secondly, longitudinal investigations should be conducted to explore the sustained effects of social media use on learning self-efficacy. Such studies would offer insights into how the observed benefits evolve over time and whether they lead to improved academic performance or other relevant outcomes. Furthermore, future research should consider the exploration of potential moderators such as individual differences in students' learning styles, prior social media experience, and psychological factors that may influence the effectiveness of social media in education. Additionally, as social media platforms continue to evolve rapidly, it is crucial to assess the impact of emerging features and trends on learning self-efficacy. This includes an examination of advanced tools like virtual reality, augmented reality, and artificial intelligence that are increasingly being integrated into social media environments. Lastly, there is a need for research exploring the development and evaluation of instructional models that effectively combine traditional teaching methods with innovative uses of social media. This could guide educators in designing courses that maximize the benefits of social media while minimizing potential drawbacks.

In conclusion, the current study marks an important step in recognizing the potential of social media as an educational tool. Through continued research, we can further unpack the mechanisms by which social media can enhance learning self-efficacy and inform the development of effective educational strategies in the digital age.

Data availability

The data that support the findings of this study are available from the corresponding authors upon reasonable request. The data are not publicly available due to privacy or ethical restrictions.

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Acknowledgements

This work is supported by the 2023 Guangxi University Young and middle-aged Teachers' Basic Research Ability Enhancement Project—“Research on Innovative Communication Strategies and Effects of Zhuang Traditional Crafts from the Perspective of the Metaverse” (Grant Nos. 2023KY0385), and the special project on innovation and entrepreneurship education in universities under the “14th Five-Year Plan” for Guangxi Education Science in 2023, titled “One Core, Two Directions, Three Integrations - Strategy and Practical Research on Innovation and Entrepreneurship Education in Local Universities” (Grant Nos. 2023ZJY1955), and the 2023 Guangxi Higher Education Undergraduate Teaching Reform General Project (Category B) “Research on the Construction and Development of PBL Teaching Model in Advertising” (Grant Nos.2023JGB294), and the 2022 Guangxi Higher Education Undergraduate Teaching Reform Project (General Category A) “Exploration and Practical Research on Public Art Design Courses in Colleges and Universities under Great Aesthetic Education” (Grant Nos. 2022JGA251), and the 2023 Guangxi Higher Education Undergraduate Teaching Reform Project Key Project “Research and Practice on the Training of Interdisciplinary Composite Talents in Design Majors Based on the Concept of Specialization and Integration—Taking Guangxi Institute of Traditional Crafts as an Example” (Grant Nos. 2023JGZ147), and the2024 Nanning Normal University Undergraduate Teaching Reform Project “Research and Practice on the Application of “Guangxi Intangible Cultural Heritage” in Packaging Design Courses from the Ideological and Political Perspective of the Curriculum” (Grant Nos. 2024JGX048),and the 2023 Hubei Normal University Teacher Teaching Reform Research Project (Key Project) -Curriculum Development for Improving Pre-service Music Teachers' Teaching Design Capabilities from the Perspective of OBE (Grant Nos. 2023014), and the 2023 Guangxi Education Science “14th Five-Year Plan” special project: “Specialized Integration” Model and Practice of Art and Design Majors in Colleges and Universities in Ethnic Areas Based on the OBE Concept (Grant Nos. 2023ZJY1805), and the 2024 Guangxi University Young and Middle-aged Teachers’ Scientific Research Basic Ability Improvement Project “Research on the Integration Path of University Entrepreneurship and Intangible Inheritance - Taking Liu Sanjie IP as an Example” (Grant Nos. 2024KY0374), and the 2022 Research Project on the Theory and Practice of Ideological and Political Education for College Students in Guangxi - “Party Building + Red”: Practice and Research on the Innovation of Education Model in College Student Dormitories (Grant Nos. 2022SZ028), and the 2021 Guangxi University Young and Middle-aged Teachers’ Scientific Research Basic Ability Improvement Project - "Research on the Application of Ethnic Elements in the Visual Design of Live Broadcast Delivery of Guangxi Local Products" (Grant Nos. 2021KY0891).

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The contribution of H. to this paper primarily lies in research design and experimental execution. H. was responsible for the overall framework design of the paper, setting research objectives and methods, and actively participating in data collection and analysis during the experimentation process. Furthermore, H. was also responsible for conducting literature reviews and played a crucial role in the writing and editing phases of the paper. L.'s contribution to this paper primarily manifests in theoretical derivation and the discussion section. Additionally, author L. also proposed future research directions and recommendations in the discussion section, aiming to facilitate further research explorations. Y.'s contribution to this paper is mainly reflected in data analysis and result interpretation. Y. was responsible for statistically analyzing the experimental data and employing relevant analytical tools and techniques to interpret and elucidate the data results.

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Hu, J., Lai, Y. & Yi, X. Effectiveness of social media-assisted course on learning self-efficacy. Sci Rep 14 , 10112 (2024). https://doi.org/10.1038/s41598-024-60724-0

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CONCEPTUAL ANALYSIS article

The effect of social media on the development of students’ affective variables.

• 1 Science and Technology Department, Nanjing University of Posts and Telecommunications, Nanjing, China
• 2 School of Marxism, Hohai University, Nanjing, Jiangsu, China
• 3 Government Enterprise Customer Center, China Mobile Group Jiangsu Co., Ltd., Nanjing, China

The use of social media is incomparably on the rise among students, influenced by the globalized forms of communication and the post-pandemic rush to use multiple social media platforms for education in different fields of study. Though social media has created tremendous chances for sharing ideas and emotions, the kind of social support it provides might fail to meet students’ emotional needs, or the alleged positive effects might be short-lasting. In recent years, several studies have been conducted to explore the potential effects of social media on students’ affective traits, such as stress, anxiety, depression, and so on. The present paper reviews the findings of the exemplary published works of research to shed light on the positive and negative potential effects of the massive use of social media on students’ emotional well-being. This review can be insightful for teachers who tend to take the potential psychological effects of social media for granted. They may want to know more about the actual effects of the over-reliance on and the excessive (and actually obsessive) use of social media on students’ developing certain images of self and certain emotions which are not necessarily positive. There will be implications for pre- and in-service teacher training and professional development programs and all those involved in student affairs.

Introduction

Social media has turned into an essential element of individuals’ lives including students in today’s world of communication. Its use is growing significantly more than ever before especially in the post-pandemic era, marked by a great revolution happening to the educational systems. Recent investigations of using social media show that approximately 3 billion individuals worldwide are now communicating via social media ( Iwamoto and Chun, 2020 ). This growing population of social media users is spending more and more time on social network groupings, as facts and figures show that individuals spend 2 h a day, on average, on a variety of social media applications, exchanging pictures and messages, updating status, tweeting, favoring, and commenting on many updated socially shared information ( Abbott, 2017 ).

Researchers have begun to investigate the psychological effects of using social media on students’ lives. Chukwuere and Chukwuere (2017) maintained that social media platforms can be considered the most important source of changing individuals’ mood, because when someone is passively using a social media platform seemingly with no special purpose, s/he can finally feel that his/her mood has changed as a function of the nature of content overviewed. Therefore, positive and negative moods can easily be transferred among the population using social media networks ( Chukwuere and Chukwuere, 2017 ). This may become increasingly important as students are seen to be using social media platforms more than before and social networking is becoming an integral aspect of their lives. As described by Iwamoto and Chun (2020) , when students are affected by social media posts, especially due to the increasing reliance on social media use in life, they may be encouraged to begin comparing themselves to others or develop great unrealistic expectations of themselves or others, which can have several affective consequences.

Considering the increasing influence of social media on education, the present paper aims to focus on the affective variables such as depression, stress, and anxiety, and how social media can possibly increase or decrease these emotions in student life. The exemplary works of research on this topic in recent years will be reviewed here, hoping to shed light on the positive and negative effects of these ever-growing influential platforms on the psychology of students.

Significance of the study

Though social media, as the name suggests, is expected to keep people connected, probably this social connection is only superficial, and not adequately deep and meaningful to help individuals feel emotionally attached to others. The psychological effects of social media on student life need to be studied in more depth to see whether social media really acts as a social support for students and whether students can use social media to cope with negative emotions and develop positive feelings or not. In other words, knowledge of the potential effects of the growing use of social media on students’ emotional well-being can bridge the gap between the alleged promises of social media and what it actually has to offer to students in terms of self-concept, self-respect, social role, and coping strategies (for stress, anxiety, etc.).

Exemplary general literature on psychological effects of social media

Before getting down to the effects of social media on students’ emotional well-being, some exemplary works of research in recent years on the topic among general populations are reviewed. For one, Aalbers et al. (2018) reported that individuals who spent more time passively working with social media suffered from more intense levels of hopelessness, loneliness, depression, and perceived inferiority. For another, Tang et al. (2013) observed that the procedures of sharing information, commenting, showing likes and dislikes, posting messages, and doing other common activities on social media are correlated with higher stress. Similarly, Ley et al. (2014) described that people who spend 2 h, on average, on social media applications will face many tragic news, posts, and stories which can raise the total intensity of their stress. This stress-provoking effect of social media has been also pinpointed by Weng and Menczer (2015) , who contended that social media becomes a main source of stress because people often share all kinds of posts, comments, and stories ranging from politics and economics, to personal and social affairs. According to Iwamoto and Chun (2020) , anxiety and depression are the negative emotions that an individual may develop when some source of stress is present. In other words, when social media sources become stress-inducing, there are high chances that anxiety and depression also develop.

Charoensukmongkol (2018) reckoned that the mental health and well-being of the global population can be at a great risk through the uncontrolled massive use of social media. These researchers also showed that social media sources can exert negative affective impacts on teenagers, as they can induce more envy and social comparison. According to Fleck and Johnson-Migalski (2015) , though social media, at first, plays the role of a stress-coping strategy, when individuals continue to see stressful conditions (probably experienced and shared by others in media), they begin to develop stress through the passage of time. Chukwuere and Chukwuere (2017) maintained that social media platforms continue to be the major source of changing mood among general populations. For example, someone might be passively using a social media sphere, and s/he may finally find him/herself with a changed mood depending on the nature of the content faced. Then, this good or bad mood is easily shared with others in a flash through the social media. Finally, as Alahmar (2016) described, social media exposes people especially the young generation to new exciting activities and events that may attract them and keep them engaged in different media contexts for hours just passing their time. It usually leads to reduced productivity, reduced academic achievement, and addiction to constant media use ( Alahmar, 2016 ).

The number of studies on the potential psychological effects of social media on people in general is higher than those selectively addressed here. For further insights into this issue, some other suggested works of research include Chang (2012) , Sriwilai and Charoensukmongkol (2016) , and Zareen et al. (2016) . Now, we move to the studies that more specifically explored the effects of social media on students’ affective states.

Review of the affective influences of social media on students

Vygotsky’s mediational theory (see Fernyhough, 2008 ) can be regarded as a main theoretical background for the support of social media on learners’ affective states. Based on this theory, social media can play the role of a mediational means between learners and the real environment. Learners’ understanding of this environment can be mediated by the image shaped via social media. This image can be either close to or different from the reality. In the case of the former, learners can develop their self-image and self-esteem. In the case of the latter, learners might develop unrealistic expectations of themselves by comparing themselves to others. As it will be reviewed below among the affective variables increased or decreased in students under the influence of the massive use of social media are anxiety, stress, depression, distress, rumination, and self-esteem. These effects have been explored more among school students in the age range of 13–18 than university students (above 18), but some studies were investigated among college students as well. Exemplary works of research on these affective variables are reviewed here.

In a cross-sectional study, O’Dea and Campbell (2011) explored the impact of online interactions of social networks on the psychological distress of adolescent students. These researchers found a negative correlation between the time spent on social networking and mental distress. Dumitrache et al. (2012) explored the relations between depression and the identity associated with the use of the popular social media, the Facebook. This study showed significant associations between depression and the number of identity-related information pieces shared on this social network. Neira and Barber (2014) explored the relationship between students’ social media use and depressed mood at teenage. No significant correlation was found between these two variables. In the same year, Tsitsika et al. (2014) explored the associations between excessive use of social media and internalizing emotions. These researchers found a positive correlation between more than 2-h a day use of social media and anxiety and depression.

Hanprathet et al. (2015) reported a statistically significant positive correlation between addiction to Facebook and depression among about a thousand high school students in wealthy populations of Thailand and warned against this psychological threat. Sampasa-Kanyinga and Lewis (2015) examined the relationship between social media use and psychological distress. These researchers found that the use of social media for more than 2 h a day was correlated with a higher intensity of psychological distress. Banjanin et al. (2015) tested the relationship between too much use of social networking and depression, yet found no statistically significant correlation between these two variables. Frison and Eggermont (2016) examined the relationships between different forms of Facebook use, perceived social support of social media, and male and female students’ depressed mood. These researchers found a positive association between the passive use of the Facebook and depression and also between the active use of the social media and depression. Furthermore, the perceived social support of the social media was found to mediate this association. Besides, gender was found as the other factor to mediate this relationship.

Vernon et al. (2017) explored change in negative investment in social networking in relation to change in depression and externalizing behavior. These researchers found that increased investment in social media predicted higher depression in adolescent students, which was a function of the effect of higher levels of disrupted sleep. Barry et al. (2017) explored the associations between the use of social media by adolescents and their psychosocial adjustment. Social media activity showed to be positively and moderately associated with depression and anxiety. Another investigation was focused on secondary school students in China conducted by Li et al. (2017) . The findings showed a mediating role of insomnia on the significant correlation between depression and addiction to social media. In the same year, Yan et al. (2017) aimed to explore the time spent on social networks and its correlation with anxiety among middle school students. They found a significant positive correlation between more than 2-h use of social networks and the intensity of anxiety.

Also in China, Wang et al. (2018) showed that addiction to social networking sites was correlated positively with depression, and this correlation was mediated by rumination. These researchers also found that this mediating effect was moderated by self-esteem. It means that the effect of addiction on depression was compounded by low self-esteem through rumination. In another work of research, Drouin et al. (2018) showed that though social media is expected to act as a form of social support for the majority of university students, it can adversely affect students’ mental well-being, especially for those who already have high levels of anxiety and depression. In their research, the social media resources were found to be stress-inducing for half of the participants, all university students. The higher education population was also studied by Iwamoto and Chun (2020) . These researchers investigated the emotional effects of social media in higher education and found that the socially supportive role of social media was overshadowed in the long run in university students’ lives and, instead, fed into their perceived depression, anxiety, and stress.

Keles et al. (2020) provided a systematic review of the effect of social media on young and teenage students’ depression, psychological distress, and anxiety. They found that depression acted as the most frequent affective variable measured. The most salient risk factors of psychological distress, anxiety, and depression based on the systematic review were activities such as repeated checking for messages, personal investment, the time spent on social media, and problematic or addictive use. Similarly, Mathewson (2020) investigated the effect of using social media on college students’ mental health. The participants stated the experience of anxiety, depression, and suicidality (thoughts of suicide or attempts to suicide). The findings showed that the types and frequency of using social media and the students’ perceived mental health were significantly correlated with each other.

The body of research on the effect of social media on students’ affective and emotional states has led to mixed results. The existing literature shows that there are some positive and some negative affective impacts. Yet, it seems that the latter is pre-dominant. Mathewson (2020) attributed these divergent positive and negative effects to the different theoretical frameworks adopted in different studies and also the different contexts (different countries with whole different educational systems). According to Fredrickson’s broaden-and-build theory of positive emotions ( Fredrickson, 2001 ), the mental repertoires of learners can be built and broadened by how they feel. For instance, some external stimuli might provoke negative emotions such as anxiety and depression in learners. Having experienced these negative emotions, students might repeatedly check their messages on social media or get addicted to them. As a result, their cognitive repertoire and mental capacity might become limited and they might lose their concentration during their learning process. On the other hand, it should be noted that by feeling positive, learners might take full advantage of the affordances of the social media and; thus, be able to follow their learning goals strategically. This point should be highlighted that the link between the use of social media and affective states is bi-directional. Therefore, strategic use of social media or its addictive use by students can direct them toward either positive experiences like enjoyment or negative ones such as anxiety and depression. Also, these mixed positive and negative effects are similar to the findings of several other relevant studies on general populations’ psychological and emotional health. A number of studies (with general research populations not necessarily students) showed that social networks have facilitated the way of staying in touch with family and friends living far away as well as an increased social support ( Zhang, 2017 ). Given the positive and negative emotional effects of social media, social media can either scaffold the emotional repertoire of students, which can develop positive emotions in learners, or induce negative provokers in them, based on which learners might feel negative emotions such as anxiety and depression. However, admittedly, social media has also generated a domain that encourages the act of comparing lives, and striving for approval; therefore, it establishes and internalizes unrealistic perceptions ( Virden et al., 2014 ; Radovic et al., 2017 ).

It should be mentioned that the susceptibility of affective variables to social media should be interpreted from a dynamic lens. This means that the ecology of the social media can make changes in the emotional experiences of learners. More specifically, students’ affective variables might self-organize into different states under the influence of social media. As for the positive correlation found in many studies between the use of social media and such negative effects as anxiety, depression, and stress, it can be hypothesized that this correlation is induced by the continuous comparison the individual makes and the perception that others are doing better than him/her influenced by the posts that appear on social media. Using social media can play a major role in university students’ psychological well-being than expected. Though most of these studies were correlational, and correlation is not the same as causation, as the studies show that the number of participants experiencing these negative emotions under the influence of social media is significantly high, more extensive research is highly suggested to explore causal effects ( Mathewson, 2020 ).

As the review of exemplary studies showed, some believed that social media increased comparisons that students made between themselves and others. This finding ratifies the relevance of the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ) and Festinger’s (1954) Social Comparison Theory. Concerning the negative effects of social media on students’ psychology, it can be argued that individuals may fail to understand that the content presented in social media is usually changed to only represent the attractive aspects of people’s lives, showing an unrealistic image of things. We can add that this argument also supports the relevance of the Social Comparison Theory and the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ), because social media sets standards that students think they should compare themselves with. A constant observation of how other students or peers are showing their instances of achievement leads to higher self-evaluation ( Stapel and Koomen, 2000 ). It is conjectured that the ubiquitous role of social media in student life establishes unrealistic expectations and promotes continuous comparison as also pinpointed in the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ).

Implications of the study

The use of social media is ever increasing among students, both at school and university, which is partly because of the promises of technological advances in communication services and partly because of the increased use of social networks for educational purposes in recent years after the pandemic. This consistent use of social media is not expected to leave students’ psychological, affective and emotional states untouched. Thus, it is necessary to know how the growing usage of social networks is associated with students’ affective health on different aspects. Therefore, we found it useful to summarize the research findings in recent years in this respect. If those somehow in charge of student affairs in educational settings are aware of the potential positive or negative effects of social media usage on students, they can better understand the complexities of students’ needs and are better capable of meeting them.

Psychological counseling programs can be initiated at schools or universities to check upon the latest state of students’ mental and emotional health influenced by the pervasive use of social media. The counselors can be made aware of the potential adverse effects of social networking and can adapt the content of their inquiries accordingly. Knowledge of the potential reasons for student anxiety, depression, and stress can help school or university counselors to find individualized coping strategies when they diagnose any symptom of distress in students influenced by an excessive use of social networking.

Admittedly, it is neither possible to discard the use of social media in today’s academic life, nor to keep students’ use of social networks fully controlled. Certainly, the educational space in today’s world cannot do without the social media, which has turned into an integral part of everybody’s life. Yet, probably students need to be instructed on how to take advantage of the media and to be the least affected negatively by its occasional superficial and unrepresentative content. Compensatory programs might be needed at schools or universities to encourage students to avoid making unrealistic and impartial comparisons of themselves and the flamboyant images of others displayed on social media. Students can be taught to develop self-appreciation and self-care while continuing to use the media to their benefit.

The teachers’ role as well as the curriculum developers’ role are becoming more important than ever, as they can significantly help to moderate the adverse effects of the pervasive social media use on students’ mental and emotional health. The kind of groupings formed for instructional purposes, for example, in social media can be done with greater care by teachers to make sure that the members of the groups are homogeneous and the tasks and activities shared in the groups are quite relevant and realistic. The teachers cannot always be in a full control of students’ use of social media, and the other fact is that students do not always and only use social media for educational purposes. They spend more time on social media for communicating with friends or strangers or possibly they just passively receive the content produced out of any educational scope just for entertainment. This uncontrolled and unrealistic content may give them a false image of life events and can threaten their mental and emotional health. Thus, teachers can try to make students aware of the potential hazards of investing too much of their time on following pages or people that publish false and misleading information about their personal or social identities. As students, logically expected, spend more time with their teachers than counselors, they may be better and more receptive to the advice given by the former than the latter.

Teachers may not be in full control of their students’ use of social media, but they have always played an active role in motivating or demotivating students to take particular measures in their academic lives. If teachers are informed of the recent research findings about the potential effects of massively using social media on students, they may find ways to reduce students’ distraction or confusion in class due to the excessive or over-reliant use of these networks. Educators may more often be mesmerized by the promises of technology-, computer- and mobile-assisted learning. They may tend to encourage the use of social media hoping to benefit students’ social and interpersonal skills, self-confidence, stress-managing and the like. Yet, they may be unaware of the potential adverse effects on students’ emotional well-being and, thus, may find the review of the recent relevant research findings insightful. Also, teachers can mediate between learners and social media to manipulate the time learners spend on social media. Research has mainly indicated that students’ emotional experiences are mainly dependent on teachers’ pedagogical approach. They should refrain learners from excessive use of, or overreliance on, social media. Raising learners’ awareness of this fact that individuals should develop their own path of development for learning, and not build their development based on unrealistic comparison of their competences with those of others, can help them consider positive values for their activities on social media and, thus, experience positive emotions.

At higher education, students’ needs are more life-like. For example, their employment-seeking spirits might lead them to create accounts in many social networks, hoping for a better future. However, membership in many of these networks may end in the mere waste of the time that could otherwise be spent on actual on-campus cooperative projects. Universities can provide more on-campus resources both for research and work experience purposes from which the students can benefit more than the cyberspace that can be tricky on many occasions. Two main theories underlying some negative emotions like boredom and anxiety are over-stimulation and under-stimulation. Thus, what learners feel out of their involvement in social media might be directed toward negative emotions due to the stimulating environment of social media. This stimulating environment makes learners rely too much, and spend too much time, on social media or use them obsessively. As a result, they might feel anxious or depressed. Given the ubiquity of social media, these negative emotions can be replaced with positive emotions if learners become aware of the psychological effects of social media. Regarding the affordances of social media for learners, they can take advantage of the potential affordances of these media such as improving their literacy, broadening their communication skills, or enhancing their distance learning opportunities.

A review of the research findings on the relationship between social media and students’ affective traits revealed both positive and negative findings. Yet, the instances of the latter were more salient and the negative psychological symptoms such as depression, anxiety, and stress have been far from negligible. These findings were discussed in relation to some more relevant theories such as the social comparison theory, which predicted that most of the potential issues with the young generation’s excessive use of social media were induced by the unfair comparisons they made between their own lives and the unrealistic portrayal of others’ on social media. Teachers, education policymakers, curriculum developers, and all those in charge of the student affairs at schools and universities should be made aware of the psychological effects of the pervasive use of social media on students, and the potential threats.

It should be reminded that the alleged socially supportive and communicative promises of the prevalent use of social networking in student life might not be fully realized in practice. Students may lose self-appreciation and gratitude when they compare their current state of life with the snapshots of others’ or peers’. A depressed or stressed-out mood can follow. Students at schools or universities need to learn self-worth to resist the adverse effects of the superficial support they receive from social media. Along this way, they should be assisted by the family and those in charge at schools or universities, most importantly the teachers. As already suggested, counseling programs might help with raising students’ awareness of the potential psychological threats of social media to their health. Considering the ubiquity of social media in everybody’ life including student life worldwide, it seems that more coping and compensatory strategies should be contrived to moderate the adverse psychological effects of the pervasive use of social media on students. Also, the affective influences of social media should not be generalized but they need to be interpreted from an ecological or contextual perspective. This means that learners might have different emotions at different times or different contexts while being involved in social media. More specifically, given the stative approach to learners’ emotions, what learners emotionally experience in their application of social media can be bound to their intra-personal and interpersonal experiences. This means that the same learner at different time points might go through different emotions Also, learners’ emotional states as a result of their engagement in social media cannot be necessarily generalized to all learners in a class.

As the majority of studies on the psychological effects of social media on student life have been conducted on school students than in higher education, it seems it is too soon to make any conclusive remark on this population exclusively. Probably, in future, further studies of the psychological complexities of students at higher education and a better knowledge of their needs can pave the way for making more insightful conclusions about the effects of social media on their affective states.

Suggestions for further research

The majority of studies on the potential effects of social media usage on students’ psychological well-being are either quantitative or qualitative in type, each with many limitations. Presumably, mixed approaches in near future can better provide a comprehensive assessment of these potential associations. Moreover, most studies on this topic have been cross-sectional in type. There is a significant dearth of longitudinal investigation on the effect of social media on developing positive or negative emotions in students. This seems to be essential as different affective factors such as anxiety, stress, self-esteem, and the like have a developmental nature. Traditional research methods with single-shot designs for data collection fail to capture the nuances of changes in these affective variables. It can be expected that more longitudinal studies in future can show how the continuous use of social media can affect the fluctuations of any of these affective variables during the different academic courses students pass at school or university.

As already raised in some works of research reviewed, the different patterns of impacts of social media on student life depend largely on the educational context. Thus, the same research designs with the same academic grade students and even the same age groups can lead to different findings concerning the effects of social media on student psychology in different countries. In other words, the potential positive and negative effects of popular social media like Facebook, Snapchat, Twitter, etc., on students’ affective conditions can differ across different educational settings in different host countries. Thus, significantly more research is needed in different contexts and cultures to compare the results.

There is also a need for further research on the higher education students and how their affective conditions are positively and negatively affected by the prevalent use of social media. University students’ psychological needs might be different from other academic grades and, thus, the patterns of changes that the overall use of social networking can create in their emotions can be also different. Their main reasons for using social media might be different from school students as well, which need to be investigated more thoroughly. The sorts of interventions needed to moderate the potential negative effects of social networking on them can be different too, all requiring a new line of research in education domain.

Finally, there are hopes that considering the ever-increasing popularity of social networking in education, the potential psychological effects of social media on teachers be explored as well. Though teacher psychology has only recently been considered for research, the literature has provided profound insights into teachers developing stress, motivation, self-esteem, and many other emotions. In today’s world driven by global communications in the cyberspace, teachers like everyone else are affecting and being affected by social networking. The comparison theory can hold true for teachers too. Thus, similar threats (of social media) to self-esteem and self-worth can be there for teachers too besides students, which are worth investigating qualitatively and quantitatively.

Probably a new line of research can be initiated to explore the co-development of teacher and learner psychological traits under the influence of social media use in longitudinal studies. These will certainly entail sophisticated research methods to be capable of unraveling the nuances of variation in these traits and their mutual effects, for example, stress, motivation, and self-esteem. If these are incorporated within mixed-approach works of research, more comprehensive and better insightful findings can be expected to emerge. Correlational studies need to be followed by causal studies in educational settings. As many conditions of the educational settings do not allow for having control groups or randomization, probably, experimental studies do not help with this. Innovative research methods, case studies or else, can be used to further explore the causal relations among the different features of social media use and the development of different affective variables in teachers or learners. Examples of such innovative research methods can be process tracing, qualitative comparative analysis, and longitudinal latent factor modeling (for a more comprehensive view, see Hiver and Al-Hoorie, 2019 ).

Author contributions

Both authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.

This study was sponsored by Wuxi Philosophy and Social Sciences bidding project—“Special Project for Safeguarding the Rights and Interests of Workers in the New Form of Employment” (Grant No. WXSK22-GH-13). This study was sponsored by the Key Project of Party Building and Ideological and Political Education Research of Nanjing University of Posts and Telecommunications—“Research on the Guidance and Countermeasures of Network Public Opinion in Colleges and Universities in the Modern Times” (Grant No. XC 2021002).

Conflict of interest

Author XX was employed by China Mobile Group Jiangsu Co., Ltd.

The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

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Keywords : affective variables, education, emotions, social media, post-pandemic, emotional needs

Citation: Chen M and Xiao X (2022) The effect of social media on the development of students’ affective variables. Front. Psychol. 13:1010766. doi: 10.3389/fpsyg.2022.1010766

Received: 03 August 2022; Accepted: 25 August 2022; Published: 15 September 2022.

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Copyright © 2022 Chen and Xiao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Miao Chen, [email protected] ; Xin Xiao, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• Open access
• Published: 16 March 2020

Exploring the role of social media in collaborative learning the new domain of learning

• Jamal Abdul Nasir Ansari 1 &
• Nawab Ali Khan 1  

Smart Learning Environments volume  7 , Article number:  9 ( 2020 ) Cite this article

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This study is an attempt to examine the application and usefulness of social media and mobile devices in transferring the resources and interaction with academicians in higher education institutions across the boundary wall, a hitherto unexplained area of research. This empirical study is based on the survey of 360 students of a university in eastern India, cognising students’ perception on social media and mobile devices through collaborative learning, interactivity with peers, teachers and its significant impact on students’ academic performance. A latent variance-based structural equation model approach was followed for measurement and instrument validation. The study revealed that online social media used for collaborative learning had a significant impact on interactivity with peers, teachers and online knowledge sharing behaviour.

Additionally, interactivity with teachers, peers, and online knowledge sharing behaviour has seen a significant impact on students’ engagement which consequently has a significant impact on students’ academic performance. Grounded to this finding, it would be valuable to mention that use of online social media for collaborative learning facilitate students to be more creative, dynamic and research-oriented. It is purely a domain of knowledge.

Introduction

The explosion of Information and Communication Technology (ICT) has led to an increase in the volume and smoothness in transferring course contents, which further stimulates the appeasement of Digital Learning Communities (DLCs). The millennium and naughtiness age bracket were Information Technology (IT) centric on web space where individual and geopolitical disperse learners accomplished their e-learning goals. The Educause Center for Applied Research [ECAR] ( 2012 ) surveyed students in higher education mentioned that students are pouring the acceptance of mobile computing devices (cellphones, smartphones, and tablet) in Higher Education Institutions (HEIs), roughly 67% surveyed students accepted that mobile devices and social media play a vital role in their academic performance and career enhancement. Mobile devices and social media provide excellent educational e-learning opportunities to the students for academic collaboration, accessing in course contents, and tutors despite the physical boundary (Gikas & Grant, 2013 ). Electronic communication technologies accelerate the pace of their encroachment of every aspect of life, the educational institutions incessantly long decades to struggle in seeing the role of such devices in sharing the contents, usefulness and interactivity style. Adoption and application of mobile devices and social media can provide ample futuristic learning opportunities to the students in accessing course contents as well as interaction with peers and experts (Cavus & Ibrahim, 2008 , 2009 ; Kukulska-Hulme & Shield, 2008 ; Nihalani & Mayrath, 2010 ; Richardson & Lenarcic, 2008 , Shih, 2007 ). Recently Pew Research Center reported that 55% American teenage age bracket of 15–17 years using online social networking sites, i.e. Myspace and Facebook (Reuben, 2008 ). Social media, the fast triggering the mean of virtual communication, internet-based technologies changed the life pattern of young youth.

Use of social media and mobile devices presents both advantages as well as challenges, mostly its benefits seen in terms of accessing course contents, video clip, transfer of the instructional notes etc. Overall students feel that social media and mobile devices are the cheap and convenient tools of obtaining relevant information. Studies in western countries have confronted that online social media use for collaborative learning has a significant contribution to students’ academic performance and satisfaction (Zhu, 2012 ). The purpose of this research project was to explore how learning and teaching activities in higher education institutions were affected by the integration and application of mobile devices in sharing the resource materials, interaction with colleagues and students’ academic performance. The broad goal of this research was to contemporise the in-depth perspectives of students’ perception of mobile devices and social media in learning and teaching activities. However, this research paper paid attention to only students’ experiences, and their understanding of mobile devices and social media fetched changes and its competency in academic performance. The fundamental research question of this research was, what are the opinions of students on social media and mobile devices when it is integrating into higher education for accessing, interacting with peers.

A researcher of the University of Central Florida reported that electronic devices and social media create an opportunity to the students for collaborative learning and also allowed the students in sharing the resource materials to the colleagues (Gikas & Grant, 2013 ). The result of the eight Egyptian universities confirmed that social media have the significant impact on higher education institutions especially in term of learning tools and teaching aids, faculty members’ use of social media seen at a minimum level due to several barriers (internet accessibility, mobile devices etc.).

Social media and mobile devices allow the students to create, edit and share the course contents in textual, video or audio forms. These technological innovations give birth to a new kind of learning cultures, learning based on the principles of collective exploration and interaction (Selwyn, 2012 ). Social media the phenomena originated in 2005 after the Web2.0 existence into the reality, defined more clearly as “a group of Internet-based applications that build on the ideological and technological foundation of web 2.0 and allow creation and exchange of user-generated contents (Kaplan & Haenlein, 2010 ). Mobile devices and social media provide opportunities to the students for accessing resources, materials, course contents, interaction with mentor and colleagues (Cavus & Ibrahim, 2008 , 2009 ; Richardson & Lenarcic, 2008 ).

Social media platform in academic institutions allows students to interact with their mentors, access their course contents, customisation and build students communities (Greenhow, 2011a , 2011b ). 90% school going students currently utilise the internet consistently, with more than 75% teenagers using online networking sites for e-learning (DeBell & Chapman, 2006 ; Lenhart, Arafeh, & Smith, 2008 ; Lenhart, Madden, & Hitlin, 2005 ). The result of the focus group interview of the students in 3 different universities in the United States confirmed that use of social media created opportunities to the learners for collaborative learning, creating and engaging the students in various extra curriculum activities (Gikas & Grant, 2013 ).

Research background and hypotheses

The technological innovation and increased use of the internet for e-learning by the students in higher education institutions has brought revolutionary changes in communication pattern. A report on 3000 college students in the United States revealed that 90% using Facebook while 37% using Twitter to share the resource materials as cited in (Elkaseh, Wong, & Fung, 2016 ). A study highlighted that the usage of social networking sites in educational institutions has a practical outcome on students’ learning outcomes (Jackson, 2011 ). The empirical investigation over 252 undergraduate students of business and management showed that time spent on twitter and involvement in managing social lives and sharing information, course-related influences their performance (Evans, 2014 ).

Social media for collaborative learning, interactivity with teachers, interactivity with peers

Many kinds of research confronted on the applicability of social media and mobile devices in higher education for interaction with colleagues.90% of faculty members use some social media in courses they were usually teaching or professional purposes out of the campus life. Facebook and YouTube are the most visited sites for the professional outcomes, around 2/3rd of the all-faculty use some medium fora class session, and 30% posted contents for students engagement in reading, view materials (Moran, Seaman, & Tinti-Kane, 2011 ). Use of social media and mobile devices in higher education is relatively new phenomena, completely hitherto area of research. Research on the students of faculty of Economics at University of Mortar, Bosnia, and Herzegovina reported that social media is already used for the sharing the materials and exchanges of information and students are ready for active use of social networking site (slide share etc.) for educational purposes mainly e-learning and communication (Mirela Mabić, 2014 ).

The report published by the U.S. higher education department stated that the majority of the faculty members engaged in different form of the social media for professional purposes, use of social media for teaching international business, sharing contents with the far way students, the use of social media and mobile devices for sharing and the interactive nature of online and mobile technologies build a better learning environment at international level. Responses on 308 graduate and postgraduate students in Saudi Arabia University exhibited that positive correlation between chatting, online discussion and file sharing and knowledge sharing, and entertainment and enjoyment with students learning (Eid & Al-Jabri, 2016 ). The quantitative study on 168 faculty members using partial least square (PLS-SEM) at Carnegie classified Doctoral Research University in the USA confirmed that perceived usefulness, external pressure and compatibility of task-technology have positive effect on social media use, the higher the degree of the perceived risk of social media, the less likely to use the technological tools for classroom instruction, the study further revealed that use of social media for collaborative learning has a positive effect on students learning outcome and satisfaction (Cao, Ajjan, & Hong, 2013 ). Therefore, the authors have hypothesized:

H1: Use of social media for collaborative learning is positively associated with interactivity with teachers.

Additionally, Madden and Zickuhr ( 2011 ) concluded that 83% of internet user within the age bracket of 18–29 years adopting social media for interaction with colleagues. Kabilan, Ahmad, and Abidin ( 2010 ) made an empirical investigation on 300 students at University Sains Malaysia and concluded that 74% students found to be the same view that social media infuses constructive attitude towards learning English (Fig. 1 ).

Research Model

Reuben ( 2008 ) concluded in his study on social media usage among professional institutions revealed that Facebook and YouTube used over half of 148 higher education institutions. Nevertheless, a recent survey of 456 accredited United States institutions highlighted 100% using some form of social media, notably Facebook 98% and Twitter 84% for e-learning purposes, interaction with mentors (Barnes & Lescault, 2011 ).

Information and communication technology (ICT), such as web-based application and social networking sites enhances the collaboration and construction of knowledge byway of instruction with outside experts (Zhu, 2012 ). A positive statistically significant relationship was found between student’s use of a variety of social media tools and the colleague’s fellow as well as the overall quality of experiences (Rutherford, 2010 ). The potential use of social media leads to collaborative learning environments which allow students to share education-related materials and contents (Fisher & Baird, 2006 ). The report of 233 students in the United States higher educations confirmed that more recluse students interact through social media, which assist them in collaborative learning and boosting their self-confidence (Voorn & Kommers, 2013 ). Thus hypotheses as

H2: Use of social media for collaborative learning is positively associated with interactivity with peers.

Social media for collaborative learning, interactivity with peers, online knowledge sharing behaviour and students’ engagement

Students’ engagement in social media and its types represent their physical and mental involvement and time spent boost to the enhancement of educational Excellency, time spent on interaction with peers, teachers for collaborative learning (Kuh, 2007 ). Students’ engagement enhanced when interacting with peers and teacher was in the same direction, shares of ideas (Chickering & Gamson, 1987 ). Engagement is an active state that is influenced by interaction or lack thereof (Leece, 2011 ). With the advancement in information technology, the virtual world becomes the storehouse of the information. Liccardi et al. ( 2007 ) concluded that 30% students were noted to be active on social media for interaction with their colleagues, tutors, and friends while more than 52% used some social media forms for video sharing, blogs, chatting, and wiki during their class time. E-learning becomes now sharp and powerful tools in information technology and makes a substantial impact on the student’s academic performance. Sharing your knowledge will make you better. Social network ties were shown to be the best predictors of online knowledge sharing intention, which in turn associated with knowledge sharing behaviour (Chen, Chen, & Kinshuk, 2009 ). Social media provides the robust personalised, interactive learning environment and enhances in self-motivation as cited in (Al-Mukhaini, Al-Qayoudhi, & Al-Badi, 2014 ). Therefore, it was hypothesised that:

H3: Use of social media for collaborative learning is positively associated with online knowledge sharing behaviour.

Broadly Speaking social media/sites allow the students to interact, share the contents with colleagues, also assisting in building connections with others (Cain, 2008 ). In the present era, the majority of the college-going students are seen to be frequent users of these sophisticated devices to keep them informed and updated about the external affair. Facebook reported per day 1,00,000 new members join; Facebook is the most preferred social networking sites among the students of the United States as cited in (Cain, 2008 ). The researcher of the school of engineering, Swiss Federal Institute of Technology Lausanne, Switzerland, designed and developed Grasp, a social media platform for their students’ collaborative learning, sharing contents (Bogdanov et al., 2012 ). The utility and its usefulness could be seen in the University of Geneva and Tongji University at both two educational places students were satisfied and accept ‘ Grasp’ to collect, organised and share the contents. Students use of social media will interact ubiquity, heterogeneous and engaged in large groups (Wankel, 2009 ). So we hypotheses

H4: More interaction with teachers leads to higher students’ engagement.

However, a similar report published on 233 students revealed that social media assisted in their collaborative learning and self-confidence as they prefer communication technology than face to face communication. Although, the students have the willingness to communicate via social media platform than face to face (Voorn & Kommers, 2013 ). The potential use of social media tools facilitates in achieving higher-level learning through collaboration with colleagues and other renewed experts in their field (Junco, Heiberger, & Loken, 2011 ; Meyer, 2010 ; Novak, Razzouk, & Johnson, 2012 ; Redecker, Ala-Mutka, & Punie, 2010 ). Academic self-efficacy and optimism were found to be strongly related to performance, adjustment and consequently both directly impacted on student’s academic performance (Chemers, Hu, & Garcia, 2001 ). Data of 723 Malaysian researchers confirmed that both male and female students were satisfied with the use of social media for collaborative learning and engagement was found positively affected with learning performance (Al-Rahmi, Alias, Othman, Marin, & Tur, 2018 ). Social media were seen as a powerful driver for learning activities in terms of frankness, interactivity, and friendliness.

Junco et al. ( 2011 ) conducted research on the specific purpose of the social media; how Twitter impacted students’ engagement, found that it was extent discussion out of class, their participation in panel group (Rodriguez, 2011 ). A comparative study conducted by (Roblyer, McDaniel, Webb, Herman, & Witty, 2010 ) revealed that students were more techno-oriented than faculty members and more likely using Facebook and such similar communication technology to support their class-related task. Additionally, faculty members were more likely to use traditional techniques, i.e. email. Thus hypotheses framed is that:

H5: More interaction with peers ultimately leads to better students’ engagement.

Social networking sites and social media are closely similar, which provide a platform where students can interact, communicate, and share emotional intelligence and looking for people with other attitudes (Gikas & Grant, 2013 ). Facebook and YouTube channel use also increased in the skills/ability and knowledge and outcomes (Daniel, Isaac, & Janet, 2017 ). It was highlighted that 90% of faculty members were using some sort of social media in their courses/ teaching. Facebook was the most visited social media sites as per study, 40% of faculty members requested students to read and views content posted on social media; majority reports that videos, wiki, etc. the primary source of acquiring knowledge, social networking sites valuable tool/source of collaborative learning (Moran et al., 2011 ). However, more interestingly, in a study which was carried out on 658 faculty members in the eight different state university of Turkey, concluded that nearly half of the faculty member has some social media accounts.

Further reported that adopting social media for educational purposes, the primary motivational factor which stimulates them to use was effective and quick means of communication technology (Akçayır, 2017 ). Thus hypotheses formulated is:

H6: Online knowledge sharing behaviour is positively associated with the students’ engagement.

Using multiple treatment research design, following act-react to increase students’ academic performance and productivity, it was observed when self–monitoring record sheet was placed before students and seen that students engagement and educational productivity was increased (Rock & Thead, 2007 ). Student engagement in extra curriculum activities promotes academic achievement (Skinner & Belmont, 1993 ), increases grade rate (Connell, Spencer, & Aber, 1994 ), triggering student performance and positive expectations about academic abilities (Skinner & Belmont, 1993 ). They are spending time on online social networking sites linked to students engagement, which works as the motivator of academic performance (Fan & Williams, 2010 ). Moreover, it was noted in a survey of over 236 Malaysian students that weak association found between the online game and student’s academic performance (Eow, Ali, Mahmud, & Baki, 2009 ). In a survey of 671 students in Jordan, it was revealed that student’s engagement directly influences academic performance, also seen the indirect effect of parental involvement over academic performance (Al-Alwan, 2014 ). Engaged students are perceptive and highly active in classroom activities, ready to participate in different classroom extra activities and expose motivation to learn, which finally leads in academic achievement (Reyes, Brackett, Rivers, White, & Salovey, 2012 ). A mediated role of students engagement seen in 1399 students’ classroom emotional climate and grades (Reyes et al., 2012 ). A statistically significant relation was noticed between online lecture and exam performance.

Nonetheless, intelligence quotient, personality factors, students must be engaged in learning activities as cited in (Bertheussen & Myrland, 2016 ). The report of the 1906 students at 7 universities in Colombia confirmed that the weak correlation between collaborative learning, students faculty interaction with academic performance (Pineda-Báez et al., 2014 ) Thus, the hypothesis

H7: Student's Engagement is positively associated with the student's academic performance.

Methodology

To check the students’ perception on social media for collaborative learning in higher education institutions, Data were gathered both offline and online survey administered to students from one public university in Eastern India (BBAU, Lucknow). For the sake of this study, indicators of interactivity with peers and teachers, the items of students engagement, the statement of social media for collaborative learning, and the elements of students’ academic performance were adopted from (AL-Rahmi & Othman, 2013 ). The statement of online knowledge sharing behaviour was taken from (Ma & Yuen, 2011 ).

The indicators of all variables which were mentioned above are measured on the standardised seven-point Likert scale with the anchor (1-Strongly Disagree, to 7-Strongly Agree). Interactivity with peers was measured using four indicators; the sample items using social media in class facilitates interaction with peers ; interactivity with teachers was measured using four symbols, the sample item is using social media in class allows me to discuss with the teacher. ; engagement was measured using three indicators by using social media I felt that my opinions had been taken into account in this class ; social media for collaborative learning was measured using four indicators collaborative learning experience in social media environment is better than in a face-to-face learning environment ; students’ academic performance was measured using five signs using social media to build a student-lecturer relationship with my lecturers, and this improves my academic performance ; online knowledge sharing behaviour was assessed using five symbols the counsel was received from other colleague using social media has increased our experience .

Procedure and measurement

A sample of 360 undergraduate students was collected by convenience sampling method of a public university in Eastern India. The proposed model of study was measured and evaluated using variance based structured equation model (SEM)-a latent multi variance technique which provides the concurrent estimation of structural and measurement model that does not meet parametric assumption (Coelho & Duarte, 2016 ; Haryono & Wardoyo, 2012 ; Lee, 2007 ; Moqbel, Nevo, & Kock, 2013 ; Raykov & Marcoulides, 2000 ; Williams, Rana, & Dwivedi, 2015 ). The confirmatory factor analysis (CFA) was conducted to ensure whether the widely accepted criterion of discriminate and convergent validity met or not. The loading of all the indicators should be 0.50 or more (Field, 2011 ; Hair, Anderson, Tatham, & Black, 1992 ). And it should be statistically significant at least at the 0.05.

Demographic analysis (Table 1 )

The majority of the students in this study were females (50.8%) while male students were only 49.2% with age 15–20 years (71.7%). It could be pointed out at this juncture that the majority of the students (53.9%) in BBAU were joined at least 1–5 academic pages for their getting information, awareness and knowledge. 46.1% of students spent 1–5 h per week on social networking sites for collaborative learning, interaction with teachers at an international level. The different academic pages followed for accessing material, communication with the faculty members stood at 44.4%, there would be various forms of the social networking sites (LinkedIn, Slide Share, YouTube Channel, Researchgate) which provide the facility of online collaborative learning, a platform at which both faculty members and students engaged in learning activities.

As per report (Nasir, Khatoon, & Bharadwaj, 2018 ), most of the social media user in India are college-going students, 33% girls followed by 27% boys students, and this reports also forecasted that India is going to become the highest 370.77 million internet users in 2022. Additionally, the majority of the faculty members use smartphone 44% to connect with the students for sharing material content. Technological advantages were the pivotal motivational force which stimulates faculty members and students to exploits the opportunities of resource materials (Nasir & Khan, 2018 ) (Fig. 2 ).

Reasons for Using Social Media

When the students were asked for what reason did they use social media, it was seen that rarely using for self-promotion, very frequently using for self-education, often used for passing the time with friends, and so many fruitful information the image mentioned above depicting.

Instrument validation

The structural model was applied to scrutinize the potency and statistically significant relationship among unobserved variables. The present measurement model was evaluated using Confirmatory Factor Analysis (CFA), and allied procedures to examine the relationship among hypothetical latent variables has acceptable reliability and validity. This study used both SPSS 20.0 and AMOS to check measurement and structural model (Field, 2013 ; Hair, Anderson, et al., 1992 ; Mooi & Sarstedt, 2011 ; Norusis, 2011 ).

The Confirmatory Factor Analysis (CFA) was conducted to ensure whether the widely accepted criterion of discriminant and convergent validity met or not. The loading of all the indicators should be 0.70 or more it should be statistically significant at least at the 0.05 (Field, 2011 ; Hair, Anderson, et al., 1992 ).

CR or CA-based tests measured the reliability of the proposed measurement model. The CA provides an estimate of the indicators intercorrelation (Henseler & Sarstedt, 2013 . The benchmark limits of the CA is 0.7 or more (Nunnally & Bernstein, 1994 ). As per Table 2 , all latent variables in this study above the recommended threshold limit. Although, Average Variance Extracted (AVE) has also been demonstrated which exceed the benchmark limit 0.5. Thus all the above-specified values revealed that our instrument is valid and effective. (See Table 2 for the additional information) (Table 3 ).

In a nutshell, the measurement model clear numerous stringent tests of convergent validity, discriminant validity, reliability, and absence of multi-collinearity. The finding demonstrated that our model meets widely accepted data validation criteria. (Schumacker & Lomax, 2010 ).

The model fit was evaluated through the Chi-Square/degree of freedom (CMIN/DF), Root Mean Residual (RMR), Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), and Goodness of fit index (GFI) and Tucker-Lewis Index (TLI). The benchmark limit of the CFI, TLI, and GFI 0.90or more (Hair et al., 2016 ; Kock, 2011 ). The model study demonstrated in the table, as mentioned above 4 that the minimum threshold limit was achieved (See Table 4 for additional diagnosis).

Path coefficient of several hypotheses has been demonstrated in Fig.  3 , which is a variable par relationship. β (beta) Coefficients, standardised partial regression coefficients signify the powers of the multivariate relationship among latent variables in the model. Remarkably, it was observed that seven out of the seven proposed hypotheses were accepted and 78% of the explained variance in students’ academic performance, 60% explained variance in interactivity with teachers, 48% variance in interactivity with peers, 43% variance in online knowledge sharing behaviour and 79% variance in students’ engagement. Social media collaborative learning has a significant association with teacher interactivity(β = .693, P  < 0.001), demonstrating that there is a direct effect on interaction with the teacher by social media when other variables are controlled. On the other hand, use of social media for collaborative learning has noticed statistically significant positive relationship with peers interactivity (β = .704, p  < 0.001) meaning thereby, collaborative learning on social media by university students, leads to the high degree of interaction with peers, colleagues. Implied 10% rise in social media use for learning purposes, expected 7.04% increase in interaction with peers.

Path Diagram

Use of social media for collaborating learning has a significant positive association with online knowledge sharing behaviour (β = .583, p  < 0.001), meaning thereby that the more intense use of social media for collaborative learning by university students, the more knowledge sharing between peers and colleagues. Also, interaction with the teacher seen the significant statistical positive association with students engagement (β = .450, p  < 0.001), telling that the more conversation with teachers, leads to a high level of students engagement. Similarly, the practical interpretation of this result is that there is an expected 4.5% increase in student’s participation for every 10% increase in interaction with teachers. Interaction with peers has a significant positive association with students engagement (β = .210, p  < 0.001). Practically, the finding revealed that 10% upturn in student’s involvement, there is a 2.1% increase in peer’s interaction. There is a significant positive association between online knowledge sharing behaviour and students engagement (β = 0.247, p  < 0.001), and finally students engagement has been a statistically significant positive relationship with students’ academic performance (β = .972, p  < 0.001), this is the clear indication that more engaged students in collaborative learning via social media leads to better students’ academic performance.

Discussion and implication

There is a continuing discussion in the academic literature that use of such social media and social networking sites would facilitate collaborative learning. It is human psychology generally that such communication media technology seems only for entertainment, but it should be noted here carefully that if such communication technology would be followed with due attention prove productive. It is essential to acknowledge that most university students nowadays adopting social media communication to interact with colleagues, teachers and also making the group be in touch with old friends and even a convenient source of transferring the resources. In the present era, the majority of the university students having diversified social media community groups like Whatsapp, Facebook pages following different academic web pages to upgrade their knowledge.

Practically for every 10% rise in students’ engagement, expected to be 2.1% increase in peer interaction. As the study suggested that students engage in different sites, they start discussing with colleagues. More engaged students in collaborative learning through social media lead better students’ academic performance. The present study revealed that for every 10% increase in student’s engagement, there would be an expected increase in student academic performance at a rate of 9.72. This extensive research finding revealed that the application of online social media would facilitate the students to become more creative, dynamics and connect to the worldwide instructor for collaborative learning.

Accordingly, the use of online social media for collaborative learning, interaction with mentors and colleagues leadbetter student’s engagement which consequently affects student’s academic performance. The higher education authority should provide such a platform which can nurture the student’s intellectual talents. Based on the empirical investigation, it would be said that students’ engagement, social media communication devices facilitate students to retrieve information and interact with others in real-time regarding sharing teaching materials contents. Additionally, such sophisticated communication devices would prove to be more useful to those students who feel too shy in front of peers; teachers may open up on the web for the collaborative learning and teaching in the global scenario and also beneficial for physically challenged students. It would also make sense that intensive use of such sophisticated technology in teaching pedagogical in higher education further facilitates the teachers and students to interact digitally, web-based learning, creating discussion group, etc. The result of this investigation confirmed that use of social media for collaborative learning purposes, interaction with peers, and teacher affect their academic performance positively, meaning at this moment that implementation of such sophisticated communication technology would bring revolutionary, drastic changes in higher education for international collaborative learning (Table 5 ).

Limitations and future direction

Like all the studies, this study is also not exempted from the pitfalls, lacunas, and drawbacks. The first and foremost research limitation is it ignores the addiction of social media; excess use may lead to destruction, deviation from the focal point. The study only confined to only one academic institution. Hence, the finding of the project cannot be generalised as a whole. The significant positive results were found in this study due to the fact that the social media and mobile devices are frequently used by the university going students not only as a means of gratification but also for educational purposes.

Secondly, this study was conducted on university students, ignoring the faculty members, it might be possible that the faculty members would not have been interested in interacting with the students. Thus, future research could be possible towards faculty members in different higher education institutions. To the authors’ best reliance, this is the first and prime study to check the usefulness and applicability of social media in the higher education system in the Indian context.

Concluding observations

Based on the empirical investigation, it could be noted that application and usefulness of the social media in transferring the resource materials, collaborative learning and interaction with the colleagues as well as teachers would facilitate students to be more enthusiastic and dynamic. This study provides guidelines to the corporate world in formulating strategies regarding the use of social media for collaborative learning.

Availability of data and materials

The corresponding author declared here all types of data used in this study available for any clarification. The author of this manuscript ready for any justification regarding the data set. To make publically available of the data used in this study, the seeker must mail to the mentioned email address. The profile of the respondents was completely confidential.

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The Impact of Social Media on the Mental Health of Adolescents and Young Adults: A Systematic Review

Abderrahman m khalaf.

1 Psychiatry Department, Saudi Commission for Health Specialties, Ministry of Health, Riyadh, SAU

Abdullah A Alubied

Ahmed m khalaf.

2 College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh, SAU

Abdallah A Rifaey

3 College of Medicine, Almaarefa University, Riyadh, SAU

Adolescents increasingly find it difficult to picture their lives without social media. Practitioners need to be able to assess risk, and social media may be a new component to consider. Although there is limited empirical evidence to support the claim, the perception of the link between social media and mental health is heavily influenced by teenage and professional perspectives. Privacy concerns, cyberbullying, and bad effects on schooling and mental health are all risks associated with this population's usage of social media. However, ethical social media use can expand opportunities for connection and conversation, as well as boost self-esteem, promote health, and gain access to critical medical information. Despite mounting evidence of social media's negative effects on adolescent mental health, there is still a scarcity of empirical research on how teens comprehend social media, particularly as a body of wisdom, or how they might employ wider modern media discourses to express themselves. Youth use cell phones and other forms of media in large numbers, resulting in chronic sleep loss, which has a negative influence on cognitive ability, school performance, and socio-emotional functioning. According to data from several cross-sectional, longitudinal, and empirical research, smartphone and social media use among teenagers relates to an increase in mental distress, self-harming behaviors, and suicidality. Clinicians can work with young people and their families to reduce the hazards of social media and smartphone usage by using open, nonjudgmental, and developmentally appropriate tactics, including education and practical problem-solving.

Introduction and background

Humans are naturally social species that depend on the companionship of others to thrive in life. Thus, while being socially linked with others helps alleviate stress, worry, and melancholy, a lack of social connection can pose major threats to one's mental health [ 1 ]. Over the past 10 years, the rapid emergence of social networking sites like Facebook, Twitter, Instagram, and others has led to some significant changes in how people connect and communicate (Table 1 ). Over one billion people are currently active users of Facebook, the largest social networking website, and it is anticipated that this number will grow significantly over time, especially in developing countries. Facebook is used for both personal and professional interaction, and its deployment has had a number of positive effects on connectivity, idea sharing, and online learning [ 2 ]. Furthermore, the number of social media users globally in 2019 was 3.484 billion, a 9% increase year on year [ 3 ].

Mental health is represented as a state of well-being in which individuals recognize their potential, successfully navigate daily challenges, perform effectively at work, and make a substantial difference in the lives of others [ 4 ]. There is currently debate over the benefits and drawbacks of social media on mental health [ 5 ]. Social networking is an important part of safeguarding our mental health. Mental health, health behavior, physical health, and mortality risk are all affected by the quantity and quality of social contacts [ 5 ].

Social media use and mental health may be related, and the displaced behavior theory could assist in clarifying why. The displaced behavior hypothesis is a psychology theory that suggests people have limited self-control and, when confronted with a challenging or stressful situation, may engage in behaviors that bring instant gratification but are not in accordance with their long-term objectives [ 6 ]. In addition, when people are unable to deal with stress in a healthy way, they may act out in ways that temporarily make them feel better but ultimately harm their long-term goals and wellness [ 7 , 8 ]. In the 1990s, social psychologist Roy Baumeister initially suggested the displaced behavior theory [ 9 ]. Baumeister suggested that self-control is a limited resource that can be drained over time and that when self-control resources are low, people are more likely to engage in impulsive or self-destructive conduct [ 9 ]. This can lead to a cycle of bad behaviors and outcomes, as individuals may engage in behaviors that bring short respite but eventually add to their stress and difficulties [ 9 ]. According to the hypothetical terms, those who participate in sedentary behaviors, including social media, engage in fewer opportunities for in-person social interaction, both of which have been demonstrated to be protective against mental illnesses [ 10 ]. Social theories, on the other hand, discovered that social media use influences mental health by affecting how people interpret, maintain, and interact with their social network [ 4 ].

Numerous studies on social media's effects have been conducted, and it has been proposed that prolonged use of social media sites like Facebook may be linked to negative manifestations and symptoms of depression, anxiety, and stress [ 11 ]. A distinct and important time in a person's life is adolescence. Additionally, risk factors such as family issues, bullying, and social isolation are readily available at this period, and it is crucial to preserve social and emotional growth. The growth of digital technology has affected numerous areas of adolescent lives. Nowadays, teenagers' use of social media is one of their most apparent characteristics. Being socially connected with other people is a typical phenomenon, whether at home, school, or a social gathering, and adolescents are constantly in touch with their classmates via social media accounts. Adolescents are drawn to social networking sites because they allow them to publish pictures, images, and videos on their platforms. It also allows teens to establish friends, discuss ideas, discover new interests, and try out new kinds of self-expression. Users of these platforms can freely like and comment on posts as well as share them without any restrictions. Teenagers now frequently post insulting remarks on social media platforms. Adolescents frequently engage in trolling for amusement without recognizing the potentially harmful consequences. Trolling on these platforms focuses on body shaming, individual abilities, language, and lifestyle, among other things. The effects that result from trolling might cause anxiety, depressive symptoms, stress, feelings of isolation, and suicidal thoughts. The authors explain the influence of social media on teenage well-being through a review of existing literature and provide intervention and preventative measures at the individual, family, and community levels [ 12 ].

Although there is a "generally correlated" link between teen social media use and depression, certain outcomes have been inconsistent (such as the association between time spent on social media and mental health issues), and the data quality is frequently poor [ 13 ]. Browsing social media could increase your risk of self-harm, loneliness, and empathy loss, according to a number of research studies. Other studies either concluded that there is no harm or that some people, such as those who are socially isolated or marginalized, may benefit from using social media [ 10 ]. Because of the rapid expansion of the technological landscape in recent years, social media has become increasingly important in the lives of young people. Social networking has created both enormous new challenges and interesting new opportunities. Research is beginning to indicate how specific social media interactions may impair young people's mental health [ 14 ]. Teenagers could communicate with one another on social media platforms, as well as produce, like, and share content. In most cases, these individuals are categorized as active users. On the other hand, teens can also use social media in a passive manner by "lurking" and focusing entirely on the content that is posted by others. The difference between active and passive social media usage is sometimes criticized as a false dichotomy because it does not necessarily reveal whether a certain activity is goal-oriented or indicative of procrastination [ 15 ]. However, the text provides no justification for why this distinction is wrong [ 16 ]. For instance, one definition of procrastination is engaging in conversation with other people to put off working on a task that is more important. The goal of seeing the information created by other people, as opposed to participating with those same individuals, may be to keep up with the lives of friends. One of the most important distinctions that can be made between the various sorts is whether the usage is social. When it comes to understanding and evaluating all these different applications of digital technology, there are a lot of obstacles to overcome. Combining all digital acts into a single predictor of pleasure would, from both a philosophical and an empirical one, invariably results in a reduction in accuracy [ 17 ].

Methodology

This systematic review was carried out and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and standard practices in the field. The purpose of this study was to identify studies on the influence of technology, primarily social media, on the psychosocial functioning, health, and well-being of adolescents and young adults.

The MEDLINE bibliographical database, PubMed, Google Scholar, CINAHL (Cumulative Index to Nursing and Allied Health Literature), and Scopus were searched between 1 January 2000 and 30 May 2023. Social media AND mental health AND adolescents AND young adults were included in the search strategy (impact or relation or effect or influence).

Two researchers (AK and AR) separately conducted a literature search utilizing the search method and evaluated the inclusion eligibility of the discovered papers based on their titles and abstracts. Then, the full texts of possibly admissible publications were retrieved and evaluated for inclusion. Disagreements among the researchers were resolved by debate and consensus.

The researchers included studies that examined the impact of technology, primarily social media, on the psychosocial functioning, health, and well-being of adolescents and young adults. We only considered English publications, reviews, longitudinal surveys, and cross-sectional studies. We excluded studies that were not written in English, were not comparative, were case reports, did not report the results of interest, or did not list the authors' names. We also found additional articles by looking at the reference lists of the retrieved articles.

Using a uniform form, the two researchers (AK and AA) extracted the data individually and independently. The extracted data include the author, publication year, study design, sample size and age range, outcome measures, and the most important findings or conclusions.

A narrative synthesis of the findings was used to analyze the data, which required summarizing and presenting the results of the included research in a logical and intelligible manner. Each study's key findings or conclusions were summarized in a table.

Study Selection

A thorough search of electronic databases, including PubMed, Embase, and Cochrane Library, was done from 1 January 2000 to 20 May 2023. Initial research revealed 326 potentially relevant studies. After deleting duplicates and screening titles and abstracts, the eligibility of 34 full-text publications was evaluated. A total of 23 papers were removed for a variety of reasons, including non-comparative studies, case reports, and studies that did not report results of interest (Figure ​ (Figure1 1 ).

PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

This systematic review identified 11 studies that examined the connection between social media use and depression symptoms in children and adolescents. The research demonstrated a modest but statistically significant association between social media use and depression symptoms. However, this relationship's causality is unclear, and additional study is required to construct explanatory models and hypotheses for inferential studies [ 18 ].

Additional research studied the effects of technology on the psychosocial functioning, health, and well-being of adolescents and young adults. Higher levels of social media usage were connected with worse mental health outcomes [ 19 ], and higher levels of social media use were associated with an increased risk of internalizing and externalizing difficulties among adolescents, especially females [ 20 ]. The use of social media was also connected with body image problems and disordered eating, especially among young women [ 21 ], and social media may be a risk factor for alcohol consumption and associated consequences among adolescents and young adults [ 22 ].

It was discovered that cyberbullying victimization is connected with poorer mental health outcomes in teenagers, including an increased risk of sadness and anxiety [ 23 ]. The use of social media was also connected with more depressive symptoms and excessive reassurance-seeking, but also with greater popularity and perceived social support [ 24 ], as well as appearance comparisons and body image worries, especially among young women [ 25 ]. Children and adolescents' bedtime media device use was substantially related to inadequate sleep quantity, poor sleep quality, and excessive daytime drowsiness [ 26 ].

Online friends can be a significant source of social support, but in-person social support appears to provide greater protection against persecution [ 27 ]. Digital and social media use offers both benefits and risks to the health of children and adolescents, and an individualized family media use plan can help strike a balance between screen time/online time and other activities, set boundaries for accessing content, promote digital literacy, and support open family communication and consistent media use rules (Tables ​ (Tables2, 2 , ​ ,3) 3 ) [ 28 ].

Does Social Media Have a Positive or Negative Impact on Adolescents and Young Adults?

Adults frequently blame the media for the problems that younger generations face, conceptually bundling different behaviors and patterns of use under a single term when it comes to using media to increase acceptance or a feeling of community [ 29 , 30 ]. The effects of social media on mental health are complex, as different goals are served by different behaviors and different outcomes are produced by distinct patterns of use [ 31 ]. The numerous ways that people use digital technology are often disregarded by policymakers and the general public, as they are seen as "generic activities" that do not have any specific impact [ 32 ]. Given this, it is crucial to acknowledge the complex nature of the effects that digital technology has on adolescents' mental health [ 19 ]. This empirical uncertainty is made worse by the fact that there are not many documented metrics of how technology is used. Self-reports are the most commonly used method for measuring technology use, but they can be prone to inaccuracy. This is because self-reports are based on people's own perceptions of their behavior, and these perceptions can be inaccurate [ 33 ]. At best, there is simply a weak correlation between self-reported smartphone usage patterns and levels that have been objectively verified [ 34 , 35 ].

When all different kinds of technological use are lumped together into a single behavioral category, not only does the measurement of that category contribute to a loss of precision, but the category also contributes to a loss of precision. To obtain precision, we need to investigate the repercussions of a wide variety of applications, ideally guided by the findings of scientific research [ 36 ]. The findings of this research have frequently been difficult to interpret, with many of them suggesting that using social media may have a somewhat negative but significantly damaging impact on one's mental health [ 36 ]. There is a growing corpus of research that is attempting to provide a more in-depth understanding of the elements that influence the development of mental health, social interaction, and emotional growth in adolescents [ 20 ].

It is challenging to provide a succinct explanation of the effects that social media has on young people because it makes use of a range of different digital approaches [ 37 , 38 ]. To utilize and respond to social media in either an adaptive or maladaptive manner, it is crucial to first have a solid understanding of personal qualities that some children may be more likely to exhibit than others [ 39 ]. In addition to this, the specific behaviors or experiences on social media that put teenagers in danger need to be recognized.

When a previous study particularly questioned teenagers in the United States, the authors found that 31% of them believe the consequences are predominantly good, 45% believe they are neither positive nor harmful, and 24% believe they are unfavorable [ 21 ]. Teens who considered social media beneficial reported that they were able to interact with friends, learn new things, and meet individuals who shared similar interests because of it. Social media is said to enhance the possibility of (i) bullying, (ii) ignoring face-to-face contact, and (iii) obtaining incorrect beliefs about the lives of other people, according to those who believe the ramifications are serious [ 21 ]. In addition, there is the possibility of avoiding depression and suicide by recognizing the warning signs and making use of the information [ 40 ]. A common topic that comes up in this area of research is the connection that should be made between traditional risks and those that can be encountered online. The concept that the digital age and its effects are too sophisticated, rapidly shifting, or nuanced for us to fully comprehend or properly shepherd young people through is being questioned, which challenges the traditional narrative that is sent to parents [ 41 ]. The last thing that needs to be looked at is potential mediators of the link between social factors and teenage depression and suicidality (for example, gender, age, and the participation of parents) [ 22 ].

The Dangers That Come With Young Adults Utilizing Social Media

The experiences that adolescents have with their peers have a substantial impact on the onset and maintenance of psychopathology in those teenagers. Peer relationships in the world of social media can be more frequent, intense, and rapid than in real life [ 42 ]. Previous research [ 22 ] has identified a few distinct types of peer interactions that can take place online as potential risk factors for mental health. Being the target of cyberbullying, also known as cyber victimization, has been shown to relate to greater rates of self-inflicted damage, suicidal ideation, and a variety of other internalizing and externalizing issues [ 43 ]. Additionally, young people may be put in danger by the peer pressure that can be found on social networking platforms [ 44 ]. This can take the form of being rejected by peers, engaging in online fights, or being involved in drama or conflict [ 45 ]. Peer influence processes may also be amplified among teenagers who spend time online, where they have access to a wider diversity of their peers as well as content that could be damaging to them [ 46 ]. If young people are exposed to information on social media that depicts risky behavior, their likelihood of engaging in such behavior themselves (such as drinking or using other drugs) may increase [ 22 ]. It may be simple to gain access to online materials that deal with self-harm and suicide, which may result in an increase in the risk of self-harm among adolescents who are already at risk [ 22 ]. A recent study found that 14.8% of young people who were admitted to mental hospitals because they posed a risk to others or themselves had viewed internet sites that encouraged suicide in the two weeks leading up to their admission [ 24 ]. The research was conducted on young people who were referred to mental hospitals because they constituted a risk to others or themselves [ 24 ]. They prefer to publish pictures of themselves on social networking sites, which results in a steady flow of messages and pictures that are often and painstakingly modified to present people in a favorable light [ 24 ]. This influences certain young individuals, leading them to begin making unfavorable comparisons between themselves and others, whether about their achievements, their abilities, or their appearance [ 47 , 48 ].

There is a correlation between higher levels of social networking in comparison and depressed symptoms in adolescents, according to studies [ 25 ]. When determining how the use of technology impacts the mental health of adolescents, it is essential to consider the issue of displacement. This refers to the question of what other important activities are being replaced by time spent on social media [ 49 ]. It is a well-established fact that the circadian rhythms of children and adolescents have a substantial bearing on both their physical and mental development.

However, past studies have shown a consistent connection between using a mobile device before bed and poorer sleep quality results [ 50 ]. These results include shorter sleep lengths, decreased sleep quality, and daytime tiredness [ 50 ]. Notably, 36% of adolescents claim they wake up at least once over the course of the night to check their electronic devices, and 40% of adolescents say they use a mobile device within five minutes of going to bed [ 25 ]. Because of this, the impact of social media on the quality of sleep continues to be a substantial risk factor for subsequent mental health disorders in young people, making it an essential topic for the continuation of research in this area [ 44 ].

Most studies that have been conducted to investigate the link between using social media and experiencing depression symptoms have concentrated on how frequently and problematically people use social media [ 4 ]. Most of the research that was taken into consideration for this study found a positive and reciprocal link between the use of social media and feelings of depression and, on occasion, suicidal ideation [ 51 , 52 ]. Additionally, it is unknown to what extent the vulnerability of teenagers and the characteristics of substance use affect this connection [ 52 ]. It is also unknown whether other aspects of the environment, such as differences in cultural norms or the advice and support provided by parents, have any bearing on this connection [ 25 ]. Even if it is probable that moderate use relates to improved self-regulation, it is not apparent whether this is the result of intermediate users having naturally greater self-regulation [ 25 ].

Gains From Social Media

Even though most of the debate on young people and new media has centered on potential issues, the unique features of the social media ecosystem have made it feasible to support adolescent mental health in more ways than ever before [ 39 ]. Among other benefits, using social media may present opportunities for humor and entertainment, identity formation, and creative expression [ 53 ]. More mobile devices than ever before are in the hands of teenagers, and they are using social media at never-before-seen levels [ 27 ]. This may not come as a surprise given how strongly young people are drawn to digital devices and the affordances they offer, as well as their heightened craving for novelty, social acceptance, and affinity [ 27 ]. Teenagers are interacting with digital technology for longer periods of time, so it is critical to comprehend the effects of this usage and use new technologies to promote teens' mental health and well-being rather than hurt it [ 53 ]. Considering the ongoing public discussion, we should instead emphasize that digital technology is neither good nor bad in and of itself [ 27 ].

One of the most well-known benefits of social media is social connection; 81% of students say it boosts their sense of connectedness to others. Connecting with friends and family is usually cited by teenagers as the main benefit of social media, and prior research typically supports the notion that doing so improves people's well-being. Social media can be used to increase acceptance or a feeling of community by providing adolescents with opportunities to connect with others who share their interests, beliefs, and experiences [ 29 ]. Digital media has the potential to improve adolescent mental health in a variety of ways, including cutting-edge applications in medical screening, treatment, and prevention [ 28 ]. In terms of screening, past research has suggested that perusing social media pages for signs of melancholy or drug abuse may be viable. More advanced machine-learning approaches have been created to identify mental disease signs on social media, such as depression, post-traumatic stress disorder, and suicidality. Self-report measures are used in most studies currently conducted on adolescent media intake. It is impossible to draw firm conclusions on whether media use precedes and predicts negative effects on mental health because research has only been conducted once. Adults frequently blame the media for the problems that younger generations face [ 30 ]. Because they are cyclical, media panics should not just be attributed to the novel and the unknown. Teenagers' time management, worldview, and social interactions have quickly and dramatically changed as a result of technology. Social media offers a previously unheard-of opportunity to spread awareness of mental health difficulties, and social media-based health promotion programs have been tested for a range of cognitive and behavioral health conditions. Thanks to social media's instant accessibility, extensive possibilities, and ability to reach remote areas, young people with mental health issues have exciting therapy options [ 54 ]. Preliminary data indicate that youth-focused mental health mobile applications are acceptable, but further research is needed to assess their usefulness and effectiveness. Youth now face new opportunities and problems as a result of the growing significance of digital media in their life. An expanding corpus of research suggests that teenagers' use of social media may have an impact on their mental health. But more research is needed [ 18 ] considering how swiftly the digital media landscape is changing.

Conclusions

In the digital era, people efficiently employ technology; it does not "happen" to them. Studies show that the average kid will not be harmed by using digital technology, but that does not mean there are no situations where it could. In this study, we discovered a connection between social media use and adolescent depression. Since cross-sectional research represents the majority, longitudinal studies are required. The social and personal life of young people is heavily influenced by social media. Based on incomplete and contradictory knowledge on young people and digital technology, professional organizations provide guidance to parents, educators, and institutions. If new technologies are necessary to promote social interaction or develop digital and relational (digitally mediated) skills for growing economies, policies restricting teen access to them may be ineffective. The research on the impact of social media on mental health is still in its early stages, and more research is needed before we can make definitive recommendations for parents, educators, or institutions. Reaching young people during times of need and when assistance is required is crucial for their health. The availability of various friendships and services may improve the well-being of teenagers.

The authors have declared that no competing interests exist.

Online social networks security and privacy: comprehensive review and analysis

• Survey and State of the Art
• Open access
• Published: 01 June 2021
• Volume 7 , pages 2157–2177, ( 2021 )

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• Ankit Kumar Jain   ORCID: orcid.org/0000-0002-9482-6991 1 ,
• Somya Ranjan Sahoo 2 &
• Jyoti Kaubiyal 1  

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With fast-growing technology, online social networks (OSNs) have exploded in popularity over the past few years. The pivotal reason behind this phenomenon happens to be the ability of OSNs to provide a platform for users to connect with their family, friends, and colleagues. The information shared in social network and media spreads very fast, almost instantaneously which makes it attractive for attackers to gain information. Secrecy and surety of OSNs need to be inquired from various positions. There are numerous security and privacy issues related to the user’s shared information especially when a user uploads personal content such as photos, videos, and audios. The attacker can maliciously use shared information for illegitimate purposes. The risks are even higher if children are targeted. To address these issues, this paper presents a thorough review of different security and privacy threats and existing solutions that can provide security to social network users. We have also discussed OSN attacks on various OSN web applications by citing some statistics reports. In addition to this, we have discussed numerous defensive approaches to OSN security. Finally, this survey discusses open issues, challenges, and relevant security guidelines to achieve trustworthiness in online social networks.

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Introduction

When the internet became popular in the mid-1990’s it made it possible to share information in ways that were never possible before. But a personal aspect was still lacking in sharing information [ 1 ]. And then in the early 2000s, social networking sites introduce a personal flavor to online information sharing which was embraced by the masses [ 2 ]. Social networking is the practice of expanding one’s contact with other individuals mostly through social media sites like Facebook, Twitter, Instagram, LinkedIn and many more [ 3 ]. It can be used for both personal and business reasons [ 4 ]. It brings people together to talk, share ideas and interests and make new friends. Basically, it helps people from different geographical regions to collaborate [ 5 ]. Social networking platforms have always been found to be easy to use. This is the reason social media sites are growing exponentially in popularity and numbers. Figure  1 shows the basic constituents of social networks and the fields in which it is playing a major role [ 6 ]. As the figure shows, social networking can be used for entertainment, building business opportunities, making a career, improving one’s social skills, and forging relationships with other individuals [ 7 ]. Facebook and Myspace are among the most preferred social networking sites Since a large chunk of the online population utilize social media platform, it has become a significant medium to promote business, awareness campaign.

Constituents of online social networks

Since people consider social media as a personal communication tool, the importance to safeguard their information stored in these social networking sites is often taken for granted. With the passage of time, people are putting more and more information in different forms on social networks which can lead to unprecedented access to people’s and business information. The amount of information stored in social networks is very enticing for adversaries whose aim is to harm someone. They can create havoc worldwide with this huge amount of information in hands. Moreover, social media has become a great medium of advertisement for marketers and if they do not take social media security issues seriously enough, they make themselves vulnerable to a wide variety of threats and put their confidential data at risk. Also, social network can be classified into many types based on their uses. Social networks can be classified into four broad classifications namely, ‘social connections’, ‘multimedia sharing’, ‘professional’ and ‘discussion forums’. This section discusses the types of social networking sites and vulnerabilities and instances of phishing that have occurred on said classifications. Current problems are also stated with an emphasis on malicious content-based phishing attacks. Figure  2 shows different types of social networking sites can broadly be classified into.

Types of social networking sites

In Social connection, People use this network to connect with people and brands online. Although there are other types of social networking sites available online, this type certainly defines social media now. Sites that come under this category are ‘Facebook’, ‘Twitter’, ‘Google + ’, ‘Myspace’. Although there are advantages of using these sites, it has some disadvantages also. These sites are vulnerable to phishing attacks in numerous ways. An intruder can make a portal that looks identical to a Facebook page. And then may lure users into entering into their credentials in different ways. Some of these methods are:

Sending fake messages which states that their Facebook account is about to be disabled in a few days.

The user may be tricked into clicking a link from the personal message sent by his friend stating that someone has uploaded personal pictures of the user in the given link.

Some attackers send a message claiming that the user’s account needs to be updated to use it further. And a link is given to download that update which contains an address of the malicious site.

Also, multimedia sharing networks are used to share pictures, videos, live videos, and other media online. They give an opportunity to users and brands to share their media online. Sites under this category are ‘YouTube’, ‘Flickr’, ‘Instagram’, ‘Snapchat’. Nowadays every social media has an “inbox” feature where anyone can send messages to their friends and chat with them. Recently, YouTube has also released this feature. This gives the attacker a great opportunity to phish his target. He can send a shortened URL in the message which redirects the user to a malicious website [ 8 ]. Since it is not easy to recognize a shortened URL, whether it is legitimate or not, attackers take advantage and obfuscate their malicious content in shortened URLs. Professional social networks are developed to provide career opportunities to their users. It may provide a general forum or may be focused on specific occupations or interest depending on the nature of the website. ‘LinkedIn’, ‘Classroom2.0’, ‘Pinterest’ are some of the examples of professional social networking sites. Since these social networking sites contain all professional information of the user including email id, an attacker can use these details to send a victim a personalized mail. These emails may be like emails claiming prize-money which contains the malicious link. Similarly, in discussion forums, people use these networks to discuss topics and share opinions. These networks are an excellent resource for market research and one of the oldest forms of social network. ‘Reddit’, ‘Quora’ and ‘Digg’ are some examples of popular discussion forums. In these forums, people also share links related to their research so that users can get more information about their topic of research. Some illegitimate users share malicious links to lead astray users to some phishing websites. In this way, phishing can also be done in discussion forums.

The lasting part of our paper is incorporated as follows. We present different statistics for OSN security in  " Statistics of online social network and media " section. Segment 3 particularizes the positive and negative impacts of online social networking. In Segment 4, we depict different threats that affect the user behavior in OSN platform. We describe the reason behind the OSN security issues in-depth in Segment 5. In  " Solutions for various threats " section, we discuss the defensive solutions for various threats. For user awareness in " Security-guidelines for OSNs user " section, we portray certain security rules to protect your system, account, and information. In the following section, i.e. in  " Open research issues and challenges " section, we portray the open research issues and challenges for OSN users. At last, we conclude our work in  " Conclusion " section.

Statistics of online social network and media

Near about 4 billion users exist in the online internet landscape [ 9 ]. Out of the total population on the internet, there are 2.7 billion monthly dynamic clients on Facebook, 330 million active users on Twitter, 320 million active users on Pinterest, as of Dec 30, 2020 [ 10 ]. Figure  3 illustrates the number of users on different social networking platforms [ 11 ]. According to a report from Zephoria, there is a 16 percent increase year over year in monthly active users of Facebook. Seven new profiles are created every second [ 12 ]. Users uploaded a total 350 million pictures per day. On average 510,000 comments are posted in every 60 s on Facebook, 298,000 statuses are updated, and 136,000 photos are uploaded. Since a huge amount of data is uploaded on Facebook, there is a high chance of having security risks. Anyone can post malicious content hidden inside multimedia data or with shortened uniform resource locators (URLs). There are around 83 million fake profiles which can be of illegitimate users or of professionals doing testing and research. Around 1 lakh websites are hacked daily [ 13 ].

Number of users on different social networking platforms

As per the data depicted in Fig.  4 , the use of social networking sites has amplified exponentially such that there is a large amount of data and information available on these sites which has increased risks of information leakage and has opened doors for several cyber-crimes like data interception, privacy spying, copyright infringement, and information fraudulence. Although some Social Networking Sites like Twitter do not allow disclosing private information to users, some experienced attackers can infer confidential information by analyzing user’s posts and the information they share online. The personal information we share online could give cybercriminals enough to get our email and passwords. We have taken cognizance of popularity and narrowed down the list of networks to keep the scope of study feasible. By extension, the chosen social networks employ state-of-the-art defence strategies. Thus, any possible attacks on these networks would employ state-of-the-art techniques. Transitively, the analysis holds relevance for other social networks as well.

Number of users on social media worldwide (year-wise)

Insights in Fig.  5 presents a positioning of the most banned sorts of hacking. It is as indicated by the reaction of adults to a survey in the United States during January 2021. It reports around 44% of the respondents accept that digital secret activities ought to have the most severe punishments.

Most punishable types of hacking in 2021

Figure  6 portrays the most vulnerable way for information breaches worldwide in 2021, sorted by share of identities exposed [ 14 ]. According to the recent report, 91.6 percent of data breaches resulted in impersonation or stolen identities.

Leading cause of data breaches worldwide in 2020

Nowadays geotagged photos are very popular. People tag their geographical locations along with their pictures and share them online. Some applications have this feature of geotagging which automatically tags the current location inside a picture until and unless the user turns it off manually. This can expose one's personal information like where one lives, where one is traveling, and invites thieves who can target one for robbery. When someone updates their status with their whereabouts on a regular basis, it can pose a threat to their life through possible stalking and robbery. According to a report by Heimdal Security, around 6 lakh Facebook accounts are hacked daily [ 15 ]. Individuals who devote more time on social media and are probable to like the posts of their close friends. The hackers take advantage of this trust. Hackers can also use social media to sway elections. The most popular attacks on social media are like-jacking, which occurs when attackers post fake Facebook like buttons to web pages, phishing sites, and spam emails. The statistics in Table 1 entail the percentage of internet users in the United States who have shared their passwords on their online accounts and to their loved ones as of May 2020. It is sorted by age group. The entire survey depicted that 74% of respondents aged more than 65 and above do not share online passwords with family and friends.

With this remarkable expansion in social networking threats and security issues, numerous specialists and security associations have proposed different solutions for alleviating them. Such solutions incorporate PhishAri for phishing detection [ 16 ], spam detection [ 17 ], GARS for cyber grooming detection [ 18 ], clickjacking detection system [ 19 ], framework to detect cyber espionage [ 20 ], SybilTrap to detect Sybil attacks [ 21 ], worm detection system to detect malware [ 22 ]. Users themselves must be alert while posting any media or information on social networking sites. A strong password should be adopted, and it must not be shared with anyone. One should check the URL while visiting a website and must not click any malicious links. These habits could help a user to some extent to be protected against various cyber-attacks on social media. Table 2 presents a collection of the greatest online information breaks via social media worldwide as of November 2020 [ 23 ].

Positive and negative effects of online social networks based on users perspective

Social media has changed the manner in which individuals see the world and collaborate with each other. The near-universal accessibility and minimal effort of long-range informal communication locales, for example, Facebook and Twitter have assisted millions to stay connected with family and friends [ 28 ]. Similar to many technological revolutions, social networks also have a negative side. We describe some of the positive and negative effects of social networking based on the researchers' perceptions described below.

Positive factors of OSN

The various positive factors that influence the user to create and use the environments are maintaining social relationship, marketing the product and platforms, rescue efforts, and finding common group of people to communicate and share the thoughts.

Maintaining social relationships Social networking sites have proven to be convenient in keeping up with the lives of others who matter to us. It helps to nurture friendship and other social relationships [ 29 ].

Marketing platform Professionals can post work experience and build a network of professionally oriented people on sites such as LinkedIn or Plaxo which are career-building social networks [ 30 ]. They help discover better job opportunities. Marketers can influence their audience by posting advertisements on social networking sites [ 31 ].

Rescue efforts Social media sites play a huge role in rescue and recovery efforts during calamities and disasters [ 32 ]. They connect people during such crucial times when the conventional societal structure has broken down. Bulletins are easily managed by social networking sites which can reunite missing family members. The public can be kept informed using utilities extended by essential service providers through online social networking. Real-time local updates on social media help government officials to better understand the circumstances and make more informed decisions.

Finding common groups Social networking sites help people find groups with common interest [ 33 ]. People can share their likes and dislikes, interests and obsessions and thought and views to these groups which contribute to an open society.

Negative factors of OSN

When the general users use the social network platform, he/she face a lot of trouble that identified by various researchers based on security parameter. Like,

Online intimidation: while making friends is easier on social media, predators can also find victims easily [ 34 ]. The anonymity provided by social networks has been a consistent issue for social media users. Earlier someone was bullied only face-to-face [ 35 ]. Nonetheless, now any individual can bully someone online anonymously.

The exploitation of private information: although creating an account on social networking sites is free of charge, they make their money mostly from the advertisements they show on their websites [ 36 ]. The data once gathered is sold to brokers in relationships without the consent of social media users. Moreover, adversaries can also extract confidential information about their targets from these websites using different attack techniques.

Isolation : social media has surely improved the connection between users but conversely it has also averted real-life social interaction [ 37 ]. People find it easier to follow the posted comments of people they know rather than personally visit or call them [ 38 ].

General addiction: by the records we can depict that social media is more addictive than cigarettes and alcohol. People often feel empty and depressed if they do not check their social media account for a full day.

This paper presents a systematic and in-depth study of threats and security issues that are current and are emerging. More precisely, this study encompasses all the conventional threats that affect the majority of the clients in social networks and most of the modern threats that are prevalent nowadays with an emphasis on teenagers and children. The principle objective of this paper is to give knowledge into the social network’s security and protection. It introduces the reader to all the possible dimensions of online social networks and issues related to them. Our analysis throws light on the prevalent open challenges and issues that need to be discussed to enhance the trustworthiness of online social networks.

The remaining paper is systematized as: " Statistics of online social network and media " section describes various threats that are currently prevalent in social media. " Positive and negative effects of online social networks based on users perspective " section provides reasons for social media security issues.  " Various threats on online social network and media " section discusses solutions that are given by various researchers, " Reasons behind online social media security issues " section consists of some security- guidelines suggested for users, some open issues and challenges in online social media is conferred in  " Solutions for various threats " section, finally, Segment 7 presents the conclusion.

Various threats on online social network and media

Being the technology-based society that we are, and with the prevalence of the internet, we have extended our interaction through the electronic world of the internet. Following are the attacks which users have been observing right from the beginning of social networks.

We have divided threats into three categories i.e. conventional threats, modern threats, and targeted threats (as shown in Fig.  7 ). Conventional threats include threats that users have been experiencing from the beginning of the social network. Modern threats are attacks that use advanced techniques to compromise accounts of users and targeted attacks are attacks that are targeted on some particular user which can be committed by any user for varied personal vendettas.

Classification of threats

Conventional threats

Spam attack.

Spam is the term used for unsolicited bulk electronic messages [ 39 ]. Although email is the conventional way to spread spam, social networking platform is more successful in spreading spam [ 40 ]. The communication details of legitimate users can easily be obtained from company websites, blogs, and newsgroup [ 27 ]. It is not difficult to convince the targeted client to read spam messages and trust it to be protected [ 41 ]. Most of the spams are commercial advertisements but they can also be used to collect sensitive information from users or may contain viruses, malware or scams [ 28 ].

Malware attack

Malware is a noxious programming which is explicitly evolved to contaminate or access a computer system, ordinarily without the information of the user [ 42 ]. An intruder can utilize numerous ways to spread malware and contaminate devices and networks [ 43 ]. For instance, malware may get installed by clicking a malicious URL, on the client’s framework or it might divert the client to a phony site which endeavors to acquire private data from the client. An attacker can inject some malicious script in URLs and clicking on that URLs can make that script run on a system that may collect sensitive information from that system [ 44 ]. In social networking platforms, the malware uses Online Social Network’s (OSN) structure to propagate itself such as the number of vertices, number of edges, average shortest path, and longest path.

A phishing attack is a kind of social engineering attack where the aggressor can acquire sensitive and confidential information like username, password and credit card details of a user through fake websites and emails which appears to be real [ 45 ]. An invader can impersonate an authentic user and may use his/her identity to send fake messages to other users via a social networking platform which contains malicious URL [ 46 ]. That URL might readdress a consumer to the phony website where it asks for personal information [ 47 ]. In the case of SNS, an assailant needs to attract the client to a phony page where he can execute a phishing attack. To accomplish this, the assailant uses different social engineering methodologies. For example, he can send a message to a user which says, “your personal pictures are shared on this website, please check!”. By clicking on that URL, the user is redirected to a fake website which looks like some legitimate social networking site.

Identity theft

In this sort of assault, the assailant utilizes someone else’s identity like social security number, mobile, number, and address, without their permission to commit attackers [ 48 ]. With the help of these details, the attacker can easily gain access to a victim's friend list and demand confidential information from them using different social engineering techniques [ 49 ]. Since the attacker impersonates a legitimate user, he can utilize that profile in any conceivable way which could seriously affect authentic clients [ 50 ].

Modern threats

Cross-site scripting attack.

Cross-site scripting is a very prevalent attack vector among infiltrators. The attack is abbreviated as XSS and is also known as “Self-XSS” [ 51 ]. Fundamentally, the attack executes a malicious JavaScript on the victim’s browser through different techniques. These are classified as persistent, reflected, and DOM-based XSS attacks [ 52 ]. The browser can be hijacked with just a single click of a button which may send a malicious script to the server [ 53 ]. This script is boomeranged back to the victim and gets executed on the browser. Attractive links and buttons in popular social media sites like Twitter and Facebook can trick the user into following URLs [ 54 ]. Worse yet, some users may feel compelled to copy and paste JavaScript containing links onto their browser's address bar [ 55 ]. These attacks can either steal information or act as spyware. Such attacks can also hijack computers to launch attacks on unsuspecting users. The real perpetrator of the attack is hidden behind the compromised machine.

Profile cloning attack

In this attack, the assaulter clones the users’ profile about which he has a prior knowledge. The attacker can use this cloned profile either in the same or in a different social networking platform to create a trusting relationship with the real user’s friends [ 56 ]. Once the connection is established, the attacker tricks the victim’s friends to believe in the validity of the fake profile and catch confidential information successfully which is not shared in their public profiles. This attack can also be used to commit other types of cyber-crimes like cyberbullying, cyber-stalking, and blackmailing [ 45 ].

In hijacking, the adversary compromises or takes control of a user’s account to carry out online frauds [ 57 ]. The sites without multifactor authentication and accounts with weak passwords are more vulnerable to hijacking as passwords can be obtained through phishing [ 58 ]. If we do not have multifactor authentication, then we lack a secondary line of defense [ 59 ]. Once an account is hijacked, the hijacker can send messages, share the malicious link, and can change the account information which could harm the reputation of the user [ 60 ].

Inference attack

Inference attack infers a handler’s confidential information which the user may not want to disclose, through other statistics that is put out by the user on some Social Networking Site (SNS) [ 61 ]. It uses data mining procedures on visibly available data like the user’s friend list and network topology [ 62 ]. Using this technique, an attacker can find an organization’s secret information or a user’s geographical and educational information [ 45 ].

Sybil attack

In Sybil attack, a node claims multiple identities in a network [ 63 ]. It can be harmful to social networking platforms as they contain a huge number of users who are coupled through a peer-to-peer network [ 64 ]. Peers are the computer frameworks which are associated with one another by means of the internet and they can share records straightforwardly without the need of a central server [ 32 ]. One online entity can make several fake identities and use those identities to distribute junk information, malware or even affect the reputation and popularity of an organization. For instance, a web survey can be manipulated utilizing various Internet Protocol (IP) delivers to submit an enormous number of votes, and aggressor can outvote a genuine client [ 33 ].

Clickjacking

Clickjacking is a procedure in which the invader deceives a user to click on a page that is different from what he intended to click [ 65 ]. It is also known as User Interface redress attack. The attacker exploits the vulnerability of the browsers to perform this attack [ 66 ]. He loads another page over the page which the user wants to access, as a transparent layer [ 67 ]. The two known variations of clickjacking are likejacking and cursorjacking. The front layer shows the substance with which the client can be baited. At the point when the client taps on that content he actually taps the like button. The more individuals like the post, the more it spreads.

In cursorjacking attacker replaces the actual cursor with a custom cursor image. The actual cursor is shifted from its actual mouse position. In this manner, the intruder can trick a consumer to click on the malicious site with clever positioning of page elements [ 68 ].

De-anonymization attack

In quite a lot of social networking sites like Twitter and Facebook, users can hide or protect their real identity before releasing any data by using an alias or fabricated name [ 69 ]. But if a third party wants to find out the real identity of the user, it can be done by simply linking the information leaked by these social networking sites [ 70 ]. They use strategies such as tracking cookies, network topologies, and user group enrollment to uncover the client’s genuine identity [ 71 ]. It is a sort of information mining method in which mysterious information is cross-referred to other information sources to re-recognize the unknown information [ 60 ]. An attacker can collect information about the group membership of a user by stealing history from their browser and by combining this history with the data collected. Thus the attacker can de-anonymize the user who visits that attacker’s website [ 72 ].

Cyber espionage

Cyber espionage is an act that uses cyber capabilities to gather sensitive information or intellectual property with the intention of communicating it to opposing parties [ 73 ]. These attacks are motivated by greed for monetary benefits and are popularly used as an integral part of military activity or as a demonstration of illegal intimidation [ 74 ]. It might bring about a loss of competitive advantage, materials, information, foundation or death toll. A social engineer can perform social engineering assaults using social networking sites. He can acquire important data like worker’s assignment, email address, and so forth utilizing social networking sites [ 75 ].

Targeted threats

• Cyberbullying

Cyberbullying is the use of electronic media such as emails, chats, phone conversations, and online social networks to bully or harass a person [ 76 ]. Unlike traditional bullying, cyberbullying is a continuous process [ 77 ]. It is continuously maintained through social media [ 78 ]. The attacker repeatedly sends intimidating messages, sexual remarks, posts rumors, and sometimes publishes embarrassing pictures or videos to harass a person [ 79 ]. He can also publish personal or private information about the victim causing embarrassment or humiliation. Cyberbullying can also happen accidentally. It is very difficult to find out the tone of the sender over text messages, instant messages, and emails. But the repeated patterns of such emails, texts, and online posts are rarely accidental [ 80 ].

• Cyber grooming

Cyber grooming is establishing an intimate and emotional relationship with the victim (usually children and adolescents) with the intention of compelling sexual abuse [ 81 ]. The principle point of cyber grooming is to acquire the trust of the youngster and through which intimate and individual information can be attained from the child [ 82 ]. The data is often voluptuous in nature through sexual conversations, pictures, and videos which gives the attacker an advantage to threaten and blackmail the child [ 83 ]. Assailants frequently approach teenagers or kids through counterfeit identity in child-friendly sites, leaving them vulnerable and uninformed of the fact that they have been drawn closer with the end goal of cyber grooming. However, the victim can also unknowingly initiate the grooming process when they get rewarding offers, for example, cash in return for contact details or personal photographs of themselves. In some cases, the victim knows about the fact that he/she is conversing with an adult which can prompt further commitment in sexual activities. However, it is with the individual under the age of consent and in this manner constitutes a crime. The anonymity and accessibility of advanced media permit groomers to move toward various youngsters simultaneously, exponentially increasing the instances of cyber grooming. Despite what might be expected, there are a couple of instances of feelings for the crime of cyber grooming worldwide, as 66% of the world's nations have no particular laws with respect to cyber grooming of children [ 84 ].

Cyberstalking

Cyberstalking is the observing of an individual by the means of internet, email or some other type of electronic correspondence that outcomes in fear of violence and interferes with the mental peace of that individual [ 85 ]. It involves the invasion of a person’s right to privacy. The attacker tracks the personal or confidential information of the victims and uses it to threaten them by continuous and persistent messages throughout the day. This conduct makes the victim exceptionally worried for his own safety and actuates a type of trouble, fear or disturbance in him [ 86 ]. Most of the individuals these days share their personal information like telephone number, place of residence, area, and schedule in their social networking profile. In addition, they likewise share their location-based data. An assailant can gather this data and use it for cyberstalking [ 87 ].

Reasons behind online social media security issues

Social media addresses one of the most unique, unstructured, and unregulated datasets anyplace in the advanced world and this scene is arising quickly all over the globe [ 88 ]. Every day millions of people upload their photos and other multimedia content on social media to share it with their friends. This is prompting the development of digital risk monitoring [ 89 ]. The development of web-based media has presented new security standards that put clients (representatives, clients, and partners) solidly in the aggressor’s line of sight. The social network has become the new digital milestone where attackers think that it's simple to target victims. It has presented one of the biggest, most powerful dangers to authoritative security. Attackers influence social media for the accompanying three reasons (as shown in Fig.  8 ):

The scale of social media: since a huge mass of people spend their time on social media for various purposes, attacks can spread like any other viral trend. The attacker can use hashtags, clickbait, and trending topics to announce their malware which might be focused on everyone or to some particular gathering of individuals. This represents a tremendous challenge for security experts to overcome physically.

Trusted nature of social media: adversaries take advantage of the trusting nature of social media. People sometimes accept an unknown friend request on the basis of mutual friends that requester has. They easily visit the link posted by their friends without thinking much about a possible security breach. Over one-third of the total population on social media acknowledge unknown friend requests, making online media perhaps the best mode for acquiring the trust of a target.

Invisibility to security team: majority of people in the world spend most of their time on social media networks. Observing this enormous populace is extremely troublesome as security teams do not have tools to broaden their perceptibility beyond a specific border into the social media domain where employees are intensively vulnerable to be compromised.

Reasons for social media security issues

Solutions for various threats

Many researchers in both academia and industries are constantly trying to find solutions for the aforementioned threats in social media. They have proposed many solutions and some approaches to combat these threats. This section provides a discussion on various methods and approaches proposed by different researchers on SNS security. We have classified solutions into two groups namely social network operator solutions and academic solutions. Figure  9 shows the classification.

Classification of solutions to threats

Social network operator solutions

Authentication mechanism.

To make sure that only a legitimate user is logging or registering in a social network and not a socialbot, several OSN uses authentication procedures such as CAPTCHA, multi-factor authentication, and photos-of-friend identification. For instance, the leading social networks like Twitter and Facebook use two-factor authentication principles. This principle uses a login password and a verification code received through a mobile device. This helps to mitigate the risk of an account being compromised and prevents an attacker from hijacking a legitimate account and posting malicious content.

Security and privacy setting

Many social networking sites provide configurable security and privacy setting to empower the client to shield their personal information from undesirable access by outsiders or applications. For instance, the Facebook client can modify their security setting and select the audience (like friends, friends of friends, and everybody) in the network who can see their details, pictures, posts, and other sensitive information. Moreover, Facebook additionally permits its users to either acknowledge or reject the access of third-party applications to their personal information. Many social networking sites are equipped with security measures that are internal to the system. They ensure users of the network against spams, counterfeit profiles, spammers, and different risks.

Report users

Online social networks protect the young generation and teenagers from being harassed by providing the facility to report any form of abuse or policy violations by any user in their network. For instance, if a user sees something on Facebook that is objectionable to the individual’s sentiments, but it doesn’t violate the Facebook terms then the user can utilize the report links to send a message to the one who posted it asking him to take it down or remove. When Facebook receives reports, it is reviewed and removed according to the Facebook community standards.

Academic research-based solutions

Phishing detection.

Phishing distresses the privacy and security of many traditional web applications such as websites, social networking sites, emails, and blogs. Consequently, several anti-phishing techniques have been developed to detect phishing attacks. Many researchers have put forward anti-phishing procedures which are based on techniques that try to identify phishing websites and phishing URLs. As phishing attacks are becoming more and more pervasive in online social networking sites, the research community has suggested specialized solutions for phishing attacks in a social networking environment. For instance, Aggarwal et al. proposed the PhishAri technique for real-time identification of phishing attacks occurring on Twitter. It utilized specific Twitter features like account age and number of followers to detect if the posted tweet is phishing or safe [ 16 ].

Cyberbullying detection

Although detecting cyberbullying is more complex than detecting racist language and spam [ 90 ], some researchers have tried to detect it using more complex document representation and additional information about victims and bullies [ 91 ].

Machine learning techniques can be applied to detect cyberbullying [ 92 ]. Rather than using only words and emoticons which expresses insults, obscenity, and typical cyberbullying words [ 93 ], it can also use some additional information like the gender and personality of the participants in a suspected cyberbullying event [ 94 ]. To deal with uncertainty and imprecision, a fuzzy rule-based system can be used which is a mathematical tool. To optimize the results genetic algorithms are the direct and stochastic methods.

For addressing the problem of online cyber grooming, machine learning techniques appear to be an effective measure. Michalopoulos et al. [ 18 ] presented the Grooming Attack Recognition System (GARS) a technique to recognize, analyze and control grooming attacks so that children could be protected against online attacks. It calculates the total risk value which identifies grooming threats to which a child is exposed by analyzing conversations by the child. A threshold is predefined for risk value and when the total risk value crosses the predefined threshold, an alarm mechanism is prompted. This alarm mechanism also simultaneously transmits an on-the-spot warning message to the parent. A colored signal is generated to warn the child about the degree of danger in a conversation. Escalante et al. [ 95 ] evaluated the use and performance of a profile to detect sexual predators. Through this evaluation, they also investigated aggressive texting.

Balduzzi et al. [ 19 ] designed and developed an automated system that can analyze web pages to protect the user against clickjacking attacks. It consists of a code that can detect overlapping clickable elements. And in addition to this solution, they also adopted the NoScript tool, which has an anti-clickjacking feature included in it. Anas et al. [ 96 ] proposed a solution in which other visual components are added which guarantees that the user is not able to proceed with his actions until and unless he has visibility over the control in place. To enable the working of this solution, the existence of a HyperText Markup Language (HTML) object containing a pattern was ensured. Some checkpoints are generated based on user interaction. User must follow those checkpoints without a single mouse click. In addition to it, a panel area shows the third-party reference identity. And to ensure the integrity of actions, user interface verification control is used. This technique can be applied in two ways, one is by generating random patterns in which the user has to follow that pattern to further propagate his action and the other way is to ask the user to draw that specific pattern which he has already registered. Microsoft introduced X-FRAME-OPTIONS, an Hyper Text Transfer Protocol (HTTP) header sent on HTTP responses, as a defense against frame busting and clickjacking in Internet Explorer 8. JavaScript can also be used as a defense against clickjacking [ 97 ].

Encryption techniques are available for devices on recent versions of Android and iOS. If a device is stolen, the thief cannot read the contents if encryption is enabled. Further, any attempts to read the information from internal or external memory is thwarted by the existence of a device password [ 98 ]. There are various technologies which can be used against stalkers like smartphone fingerprint lock antivirus, specialized stalker app detection software, firewalls, and privacy guards. Device encryption can be used against spyware, stalker apps and device theft [ 98 ]. Frommholz et al. have described machine learning techniques for detecting cyberstalking using textual analysis altogether [ 99 ].

Cyber espionage is a kind of targeted attack. Sahoo et al. described the concept of an ATA detection framework and introduced a system design checklist which is explicitly designed for identifying targeted attacks [ 20 ]. Organizations can create their own team to fight against targeted attacks and analyze vulnerabilities, in their and as well as in other companies’ code. Google has its own team to analyze vulnerabilities and bugs in their code. Each company has its own profile that is different from each other. So, each company must take appropriate steps according to their profile to implement security measures to design and implement security controls to address various security risks. Organizations can also be secured to some extent against targeted attacks by means of authentication systems. Earlier only password was used to protect the data, but now a two-factor authentication system is used which is a combination of password and some pin or biometric details. It is more secure than using a single factor i.e. password. The data which is no longer required for business purposes should be removed from the company's network. Keeping those records may create the risk of unauthorized access to sensitive information in an organization [ 100 ].

Fake profile

The author in Ref. [ 101 ] describes one model to distinguish the counterfeit accounts and profiles. They extracted some user profile contents from LinkedIn platform and processed those profiles content to extract different features. Subsequent to preprocessing of profiles through principal component, a training set is created utilizing the resilient backpropagation algorithm in a neural network. Support Vector Machines (SVMs) is utilized for characterization of profile. The author in Ref. [ 102 ] proposed a model that detects bot net using adaptive multilayered-based machine learning approach. The proposed work presented a bot detection framework based on decision trees which effectively detects P2P botnets. Also, the author in Ref. [ 103 ] proposed an ensemble classification model for the detection of fake news that has achieved a better accuracy compared to the other state-of-the-art. The proposed model extracts important features from the fake news datasets, and the extracted features are then classified using the ensemble model comprising of three popular machine learning models namely, decision tree, random forest, and extra tree classifier. Furthermore, the author in Ref. [ 104 ] presented a systematic literature review of existing clone node detection schemes with some theoretical and analytical survey of the existing centralized and distributed schemes for the detection of clone nodes in static WSNs environment.

Sybil detection

Al-Qurishi et al. [ 105 ] proposed a new Sybil detection system that uses a deep learning model to predict a Sybil attack accurately. This model consists of three modules namely, one data harvesting module, one feature extracting module and a deep regression model. All these three modules work in a systematic form together to analyze a user’s profile on Twitter. Rahman et al. gave a model named SybilTrap which is a graph-based semi-supervised learning system that uses both content-based and structure-based techniques to detect Sybil attacks. It is based on a semi-supervised algorithm which utilizes the interaction graph information of a node where labeled information of nodes flows through unlabeled nodes. It gathers information about the network and its users and uses this information to detect malicious users. This system is resistant to various strategic attacks such as targeted or random attacks. It is designed to work under any condition and is applicable to all existing social networks regardless of their level of trust [ 21 ].

Spam detection

Rathore et al. proposed a framework called SpamSpotter to solve the issue of spam attack on Facebook. It is based on the intelligent decision support system (IDSS). It gathers all relevant information from the user profile with the help of a decision process in IDSS and then analyzes it by mapping user data to the classification of a user profile as a spammer or legitimate. It resolves some of the issues and challenges (1) It solves the issue of an inadequate set of features that exist in most of spammer detection system. (2) It resolves the issue of uncertainty about critical pieces of Facebook information and public unavailability. (3) The use of the IDSS system resolves the issue of low accuracy and high response time. The use of machine learning classifiers in IDSS provides fast response time that is very essential to detecting spam on Facebook [ 17 ].

Faghani and Saidi [ 106 ] found that the visiting behavior of the social network members affects the propagation of XSS worms. The worm propagates slower when members mostly visit their friends rather than strangers. It can also be slowed down by the clustered nature of social networks. This is so because infected profiles in the early stages of XSS worm propagation lead to faster propagation of worm. Xu et al. [ 22 ] developed an approach to detect worms which leverages properties of online social network and propagation characteristics of OSN worms. It first builds a surveillance network based on the properties of the social graph to gather evidence against suspicious worm propagation. It monitors only a small fraction of user accounts to maximize surveillance coverage. To ensure that noise is absent in a surveillance network, a scheme is further proposed. Table 3 represents the probability of encountering different types of threats in different platforms discussed in “ Introduction ” section. It shows that the platforms used for social connections are the most vulnerable among all platforms.

Other contributions

The author in Ref. [ 107 ] proposed a novel algorithm to reform any traffic domain into a complex network using the principles of decentralized Social Internet of Things (SIoT). With the help of social networking, concepts integrate into the Internet of Things (IoT), the concept of SIoT has been proposed. The idea of the article is, every vehicle acts as a smart thing, communicate with nearby vehicles within a particular distance in a decentralized manner and together form a complex network. Also, the author in Ref. [ 108 ] proposed propose a privacy-preserving ICN forwarding scheme based on homomorphic encryption for wireless ad hoc networks to protect the private information of the user. The trust-based model proposed by the author in Ref. [ 109 ]. The author proposed a secure trusted hypothetical mathematical model for ensuring secure communication among devices by computing the individual trust of each node. In addition to this, the author proposed a decision-making model, that integrated with the hypothetical model for further speeding up the real-time communication decision within the network.

Comparative analysis with other state of art techniques

This section compared our survey related to different threat analysis and their defensive approaches with other state of art techniques and survey to show the novelty shows in Table 4 .

Security-guidelines for OSNs user

Nowadays, online social media and network have become an integral part of everyone’s life. As the reputation of these social sites grows, so do the risks of using them. The number of users increases exponentially every year. So, it becomes a necessity to secure users on these platforms. Below are some security-guidelines for users which they can practice keeping themselves reasonably secure. We have tried to give security-guidelines in two ways. First, it has been described in a general form and then it is described platform-wise (as shown in Fig.  10 ).

Security guidelines for users

General guidelines

Use a strong password: for maintaining the security of accounts, users should choose a strong password. It should not be too short as short passwords can be easily guessed. It should be long enough and must contain alphanumeric values with some special characters [ 119 ]. Users should not use the same password which they use for other accounts because if somehow an attacker gets to know that password, he can compromise all accounts of that user. So, choosing a strong password can help a user safeguard their account and profile from unauthorized access [ 120 ].

Limit location sharing: nowadays sharing location has become a trend. Many social networking sites have also introduced the feature of geotagging which automatically tags the geographical location of the user when the user uploads any multimedia on social media [ 121 ]. The user has to switch it to manual so that it does not tag location automatically. Sharing location online makes a user vulnerable to real-life crimes like robbery. So, to mitigate this risk, the user can post his location at a later point of time post completion of the visit [ 122 ]. Users must upload their multimedia content online very carefully as it may contain sensitive metadata and it is recommended to switch geotagging to manual mode in all their mobile devices and accounts. Also suggested is the use of software that removes such metadata from the pictures before uploading.

Be selective with friend requests: it is seen that many users accept friend requests without analyzing the complete profile of the requester. People generally accept friend request based on mutual friends. If the requester has some mutual friends, then they accept it [ 123 ]. Sometimes attackers make their profile attractive deliberately or they may impersonate an account. So, if the person sending a friend request is unknown, one should ignore that friend request. It could be a fake account attempting to steal sensitive information.

Be careful about what you share: users should be careful about their posts as it may reveal their personal information and sometimes others also. Many organizations keep strict rules and regulations for sharing information and multimedia content. There are many reports of people getting fired from their job due to sharing information illegally. This situation can be avoided if employees are well informed about the protocols of the organization they are working in regarding pictures, videos, and messages that they post online. Sharing information illegitimately can harm an organization’s reputation in the market along with its data and intellectual property also.

Be aware of links and third-party applications: illegitimate users can get access to someone’s account and get sensitive information by sharing a malicious link. Nowadays shortened URLs are becoming very popular on various social media platforms. These shortened URLs may be obfuscated with malicious code or script. These scripts try to gather the personal and confidential information of a user which may breach the privacy of that user. Moreover, hackers may take advantage of vulnerabilities present in a third-party application that is integrated with many popular social networks [ 124 ]. An example of such a third-party application happens to be games that are playable on online social networks which ask for user’s public information to consume their services. This gathered information may be provided to outsiders or third-party interventions. To avoid this risk, user should be careful while installing third-party applications in their profile.

Install internet security software: some threats whose pattern is known may easily be detected through anti-viruses. Threats like cyber grooming, cyberbullying can be detected to some extent by using anti-virus software [ 125 ]. Many malicious links can be shared by our friends unknowingly which redirects the user to some phishing website. Anti-virus software should be kept updated regularly due to the presence of many viruses created by hackers on a daily basis. Some social networking sites also have their own security tools which can be used by users to protect themselves from cyber-attacks.

Platform-wise

For professional networks.

Professional networks are primarily used to create contacts and increase perceptibility to potential recruitment companies [ 126 ]. So, to be safe on professional networks, one should look for the details provided by other users before adding them to one’s contact list. Generally, an adversary does not provide many details about his career.

A user should check if there are any spelling or grammar mistakes in someone's profile because if someone is applying for some job, it should be very well written and should be free from any spelling or grammar mistakes [ 127 ]. It should contain good information about that person.

Checking for consistency in a person’s career can be a good practice if a user wants to be safe on a professional network. A profile which continually and definitely changes over a short span of time is the most used part as a draw by the invader. At the point when the fraudster needs to target one sort of organization or vertical, he simply adds a new position that could be pertinent to his targets.

One should also cross-check information. If a person claims to be from the employer’s company, the user can check the company’s directory and should not hesitate to verify from his company’s human resource department.

For multimedia sharing platform

One should not post sensitive information in their photos or caption [ 3 ]. Exposing too much private information in a profile can be dangerous.

Sharing current locations on social media should be avoided. Geotagging services provided by different multimedia platforms should be turned off manually. There have been plenty of cases of thieves that were tipped off to rob homes. Suspects use social media to gather information about victims who share their location online. People who leave for a short holiday and brag about it online may come home to find the place emptied.

If an application is not in use for a long period, it is better to revoke access to that application. There are so many third-party applications which use social media account to log-in. For security and privacy concerns, one should allow access to applications that are trustworthy [ 4 ].

Enable two-step authentication for all your social media accounts wherever possible. This provides an extra layer of security to the account. In case an adversary finds out the password of a user, he will still need a second factor to authenticate himself. The second factor consists of a unique, time-sensitive code that users receive via text on their mobile phone.

For social connection platform

Users should learn about the privacy and security setting for different social media platforms and use them [ 128 ]. Each platform has its own privacy and security setting. Every platform provides settings, configuration, and privacy sections to limit who and what groups can see various aspects of the user’s profile. The privacy setting provided by the sites as default should not be adopted as it is.

The more details provided, the easier it is for an adversary to use that information to steal identity or to commit other cybercrimes. Thus, information sharing should be limited.

Before accepting a friend request, one should completely check the profile of the requester. One can make different groups for sharing different kinds of information like a different group for colleagues and family.

Before posting any information on the profile, employees should know their company’s policy over sharing any content online on social networks.

For discussion forums

One should pay attention while clicking on links given by various authors. It may be some suspicious site trying to get the credentials of the user.

Users should always keep an eye on the site’s URL. Noxious sites may look compellingly indistinguishable from a real one, however, the URL may contain slight inconsistencies like the variety in spelling or an alternate domain (e.g., .com versus.net) [ 129 ].

Be careful about communications that requests the client to act promptly, offers something that sounds unrealistic or requests personal information.

Open research issues and challenges

Scientists and researchers have found many methods and solutions to secure users on social media but there are still some issues which are not resolved. In this section, we discuss some of those issues and challenges.

Unfortunately, social networking sites are the easiest way for an attacker to lie about his identity and target the victim. They can lie about their age, looks, and can project themselves as a completely different identity according to their target. Child predators are taking advantage of this drawback in social networking sites, as children are a very easy target on these social platforms. These platforms have millions of users and monitoring each user can be very difficult. Therefore, there is a need for some system which can detect child predators effectively. Although the research community is trying to solve this issue, we need a good and effective system which can stop cyber grooming more efficiently. One possible addition to the already existing systems would be to incorporate artificial intelligence. The chat system can be improved to analyze conversations and derive meaningful inferences to support decision-making.

Social networking sites make money by allowing other companies to show advertisements on their website. Every time a user clicks an advertisement, it takes the user to a page where the user can buy a product and the social networking site get a percentage of that sale. These sites collect data of the users each time they use them so that they can show the advertisements as per the user’s interest. In this way, these social networking sites are collecting a huge amount of personal data of the user which can be sold to hundreds of businesses without user's knowledge. Hence, the user’s personal data is at risk. One possible way to thwart such data leaks is to inform the user of the data being shared. This would involve non-technical aspects to enforce a law or contract that all advertisements should abide by. From a technical standpoint there is not much control as to what the parent site decides to share with the advertising agency. Client-side browser restrictions could also provide wrapper-level security.

Nowadays surveys and games are becoming very popular on social media [ 130 ]. Generally, these surveys involve entering credentials which are supposed to enable the data for the survey to be gathered or the results to be shared. And while these surveys are collecting credentials, adversaries can skim those details to compromise user’s account.

Due to character count limitations on Twitter, people use shortened URLs to share their multimedia content. Adversaries can easily obfuscate malicious sites on these shortened URLs. This is an alarming situation since other social media applications like WhatsApp also have users who have started sharing shortened URLs. However, some social networking sites are working on this issue and have given solutions, but it is as yet conceivable that URL redirection can be used to hop from a safe landing point to a risky landing point. Again, a central repository of phishing sites could be leveraged by the client browser to warn the user when landing on the suspicious website. Further research could be conducted towards preemptive solutions that can parse URLs and warn the user even before clicking. A system is needed which can detect the malicious site from the shortened URLs effectively leveraging the already existing solutions.

Business-oriented networks contain significant business data that can be utilized to perform social engineering attacks. Some LinkedIn invitation update messages have been referred to be utilized as URL redirectors which can divert clients to some vindictive pages. This issue should be resolved so that users can be protected from a targeted attack. Here, intelligent language parsers could be trained to detect sensitive information and warn the originator of the information. Content detection can be applied to such platforms to find malicious activity. It can detect the number of posts posted through a profile because generally, the adversary posts similar messages.

There is a need to secure users on discussion forums also. Users can be easily fooled on discussion forums through phishing attacks which could result in deteriorating user trust on these forums. URL detection and filtering can be applied for these forums also to protect a user from malicious activity. Although such scenarios usually inform the user that they are moving out of the parent domain. The cost to reward ratio here is poor for any forum to implement parsers to parse external links. An incentive-based solution can be thought of to reward sites that scan external links.

Online social networks have become a vital part of the vast internet penetrated world. The paradigm shift has enabled social networks to engage with users on a daily basis. The increased rate of social media usage has solicited the need to make its users aware of the pitfalls, threats, attacks, and privacy issues in them. With the advancement in technology, social media has taken various forms. Individuals can connect to each other in a myriad of ways. Through professional sites, discussion forums, multimedia sharing networks, and many more, netizens can find themselves at the pinnacle of connectivity. Unfortunately, lack of awareness among users regarding security and privacy has the potential to lead to various cyber-attacks through social media. Although academia has come up with innovative solutions to address the security measures that are concerned with social media security, they suffer from a lack of real-world implementation and feasibility. Thus, there is a compelling need to continuously and iteratively review security issues in social networks keeping in pace with technological advancement. In this paper, we presented different scenarios related to online social network threats and their solutions using different models, frameworks, and encryption techniques that protect the social network users against various attacks. We have outlined different solutions and comparative analysis of different survey for better clarity about our survey. However, many of these privacy issues are not yet resolved. In addition to the defensive solutions, parents must monitor the kids actively when they are using internet services like OSNs. Overall, researchers can play a significant role in the defensive approach against these attacks in OSNs but still, some issues need to be resolved by using some hybrid approach, framework, and threat detection tools.

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Jain, A.K., Sahoo, S.R. & Kaubiyal, J. Online social networks security and privacy: comprehensive review and analysis. Complex Intell. Syst. 7 , 2157–2177 (2021). https://doi.org/10.1007/s40747-021-00409-7

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Social media's growing impact on our lives

Media psychology researchers are beginning to tease apart the ways in which time spent on social media is, and is not, impacting our day-to-day lives.

Social media use has skyrocketed over the past decade and a half. Whereas only five percent of adults in the United States reported using a social media platform in 2005, that number is now around 70 percent .

Growth in the number of people who use Facebook, Instagram, Twitter, and Snapchat and other social media platforms — and the time spent on them—has garnered interest and concern among policymakers, teachers, parents, and clinicians about social media's impacts on our lives and psychological well-being.

While the research is still in its early years — Facebook itself only celebrated its 15 th birthday this year — media psychology researchers are beginning to tease apart the ways in which time spent on these platforms is, and is not, impacting our day-to-day lives.

Social media and relationships

One particularly pernicious concern is whether time spent on social media sites is eating away at face-to-face time, a phenomenon known as social displacement .

Fears about social displacement are longstanding, as old as the telephone and probably older. “This issue of displacement has gone on for more than 100 years,” says Jeffrey Hall, PhD, director of the Relationships and Technology Lab at the University of Kansas. “No matter what the technology is,” says Hall, there is always a “cultural belief that it's replacing face-to-face time with our close friends and family.”

Hall's research interrogates that cultural belief. In one study , participants kept a daily log of time spent doing 19 different activities during weeks when they were and were not asked to abstain from using social media. In the weeks when people abstained from social media, they spent more time browsing the internet, working, cleaning, and doing household chores. However, during these same abstention periods, there was no difference in people's time spent socializing with their strongest social ties.

The upshot? “I tend to believe, given my own work and then reading the work of others, that there's very little evidence that social media directly displaces meaningful interaction with close relational partners,” says Hall. One possible reason for this is because we tend to interact with our close loved ones through several different modalities—such as texts, emails, phone calls, and in-person time.

What about teens?

When it comes to teens, a recent study by Jean Twenge , PhD, professor of psychology at San Diego State University, and colleagues found that, as a cohort, high school seniors heading to college in 2016 spent an “ hour less a day engaging in in-person social interaction” — such as going to parties, movies, or riding in cars together — compared with high school seniors in the late 1980s. As a group, this decline was associated with increased digital media use. However, at the individual level, more social media use was positively associated with more in-person social interaction. The study also found that adolescents who spent the most time on social media and the least time in face-to-face social interactions reported the most loneliness.

While Twenge and colleagues posit that overall face-to-face interactions among teens may be down due to increased time spent on digital media, Hall says there's a possibility that the relationship goes the other way.

Hall cites the work of danah boyd, PhD, principal researcher at Microsoft Research  and the founder of Data & Society . “She [boyd] says that it's not the case that teens are displacing their social face-to-face time through social media. Instead, she argues we got the causality reversed,” says Hall. “We are increasingly restricting teens' ability to spend time with their peers . . . and they're turning to social media to augment it.”

According to Hall, both phenomena could be happening in tandem — restrictive parenting could drive social media use and social media use could reduce the time teens spend together in person — but focusing on the latter places the culpability more on teens while ignoring the societal forces that are also at play.

The evidence is clear about one thing: Social media is popular among teens. A 2018 Common Sense Media report found that 81 percent of teens use social media, and more than a third report using social media sites multiple times an hour. These statistics have risen dramatically over the past six years, likely driven by increased access to mobile devices. Rising along with these stats is a growing interest in the impact that social media is having on teen cognitive development and psychological well-being.

“What we have found, in general, is that social media presents both risks and opportunities for adolescents,” says Kaveri Subrahmanyam, PhD, a developmental psychologist, professor at Cal State LA, and associate director of the Children's Digital Media Center, Los Angeles .

Risks of expanding social networks

Social media benefits teens by expanding their social networks and keeping them in touch with their peers and far-away friends and family. It is also a creativity outlet. In the Common Sense Media report, more than a quarter of teens said that “social media is ‘extremely' or ‘very' important for them for expressing themselves creatively.”

But there are also risks. The Common Sense Media survey found that 13 percent of teens reported being cyberbullied at least once. And social media can be a conduit for accessing inappropriate content like violent images or pornography. Nearly two-thirds of teens who use social media said they “'often' or ‘sometimes' come across racist, sexist, homophobic, or religious-based hate content in social media.”

With all of these benefits and risks, how is social media affecting cognitive development? “What we have found at the Children's Digital Media Center is that a lot of digital communication use and, in particular, social media use seems to be connected to offline developmental concerns,” says Subrahmanyam. “If you look at the adolescent developmental literature, the core issues facing youth are sexuality, identity, and intimacy,” says Subrahmanyam.

Her research suggests that different types of digital communication may involve different developmental issues. For example, she has found that teens frequently talked about sex in chat rooms , whereas their use of blogs and social media appears to be more concerned with self-presentation and identity construction.

In particular, exploring one's identity appears to be a crucial use of visually focused social media sites for adolescents. “Whether it's Facebook, whether it's Instagram, there's a lot of strategic self presentation, and it does seem to be in the service of identity,” says Subrahmanyam. “I think where it gets gray is that we don't know if this is necessarily beneficial or if it harms.”

Remaining questions

“It's important to develop a coherent identity,” she says. “But within the context of social media — when it's not clear that people are necessarily engaging in real self presentation and there's a lot of ideal-self or false-self presentation — is that good?”

There are also more questions than answers when it comes to how social media affects the development of intimate relationships during adolescence. Does having a wide network of contacts — as is common in social media—lead to more superficial interactions and hinder intimacy? Or, perhaps more important, “Is the support that you get online as effective as the support that you get offline?” ponders Subrahmanyam. “We don't know that necessarily.”

Based on her own research comparing text messages and face-to-face interactions, she says: “My hypothesis is that maybe digital interactions may be a little more ephemeral, they're a little more fleeting, and you feel good, but that the feeling is lost quickly versus face-to-face interaction.”

However, she notes that today's teens — being tech natives — may get less hung up on the online/offline dichotomy. “ We tend to think about online and offline as disconnected, but we have to recognize that for youth . . . there's so much more fluidity and connectedness between the real and the physical and the offline and the online,” she says.

In fact, growing up with digital technology may be changing teen brain development in ways we don't yet know — and these changes may, in turn, change how teens relate to technology. “Because the exposure to technology is happening so early, we have to be mindful of the possibility that perhaps there are changes happening at a neural level with early exposure,” says Subrahmanyam. “How youths interact with technology could just be qualitatively different from how we do it.”

In part two of this article , we will look at how social media affects psychological well-being and ways of using social media that are likely to amplify its benefits and decrease its harms.

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• Introduction
• Conclusions
• Article Information

Mortality rate is defined as the number of deaths per 100 000 women per year. Cluster type refers to features of counties from Local Moran I statistics surrounded by counties with alike features, and outliers as counties surrounded by counties with different features—eg, high-high clusters indicate counties with high breast cancer mortality rates surrounded by counties that also had high rates, and high-low outliers indicate counties with high breast cancer mortality surrounded by counties with low rates.

Panel A, obesity and breast cancer mortality are positively associated; the association is spatially stationary across the US, although the effect size of the association is greater in the South. Panel B, mammogram testing and breast cancer mortality are negatively associated; the association is spatially stationary across the US, although the effect size is observed in the East.

Panel A, the association between food environment index and breast cancer mortality was spatially nonstationary, with the largest negative effect sizes in Louisiana, Arkansas, Alabama, North and South Carolina, and Virginia. B, the association between food environment index and breast cancer mortality is spatially nonstationary, with the largest negative effect sizes in central US and Florida.

eAppendix. Data Processing and Aggregation

eTable 1. Variables, Description, Source, and Year

eFigure 1. Variable Selection Process

eTable 2. OLS Results Using Crude Female Breast Cancer Mortality as the Dependent Variable

eTable 3. MGWR Results Using Crude Female Breast Cancer Mortality as the Dependent Variable

eFigure 2. MGWR Standardized Beta Coefficients Describing the Association Between Crude Breast Cancer Mortality (Person Years at Risk) and Obesity, Food Index, Mammogram Testing, and Female Population Over Age 65

eFigure 3. MGWR Standardized Beta Coefficients Describing the Association Between Crude Breast Cancer Mortality (Person Years at Risk) and Exercise, Primary Care Physicians’ Ratio, Unemployment, and Income Inequality

eReferences.

Data Sharing Statement

• Analysis of Breast Cancer Mortality in the US—1975 to 2019 JAMA Original Investigation January 16, 2024 This simulation study estimates the association of breast cancer screening, treatment of stage I-III breast cancer, and treatment of metastatic breast cancer on changes in mortality due to breast cancer in US women for the period 1975-2019. Jennifer L. Caswell-Jin, MD; Liyang P. Sun, MSc; Diego Munoz, PhD; Ying Lu, PhD; Yisheng Li, PhD; Hui Huang, MS; John M. Hampton, MSc; Juhee Song, PhD; Jinani Jayasekera, PhD; Clyde Schechter, MD, MA; Oguzhan Alagoz, PhD; Natasha K. Stout, PhD; Amy Trentham-Dietz, PhD; Sandra J. Lee, ScD; Xuelin Huang, PhD; Jeanne S. Mandelblatt, MD, MPH; Donald A. Berry, PhD; Allison W. Kurian, MD, MSc; Sylvia K. Plevritis, PhD

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Anderson T , Herrera D , Mireku F, et al. Geographical Variation in Social Determinants of Female Breast Cancer Mortality Across US Counties. JAMA Netw Open. 2023;6(9):e2333618. doi:10.1001/jamanetworkopen.2023.33618

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Geographical Variation in Social Determinants of Female Breast Cancer Mortality Across US Counties

• 1 Department of Geography and Geoinformation Science, George Mason University, Fairfax, Virginia
• 2 Department of Environmental Science and Technology, University of Maryland, College Park
• 3 Department of Environmental Science and Policy, George Mason University, Fairfax, Virginia
• 4 Department of Statistics, George Mason University, Fairfax, Virginia
• 5 Department of Global and Community Health, George Mason University, Fairfax, Virginia
• 6 School of Systems Biology, Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, Virginia
• Original Investigation Analysis of Breast Cancer Mortality in the US—1975 to 2019 Jennifer L. Caswell-Jin, MD; Liyang P. Sun, MSc; Diego Munoz, PhD; Ying Lu, PhD; Yisheng Li, PhD; Hui Huang, MS; John M. Hampton, MSc; Juhee Song, PhD; Jinani Jayasekera, PhD; Clyde Schechter, MD, MA; Oguzhan Alagoz, PhD; Natasha K. Stout, PhD; Amy Trentham-Dietz, PhD; Sandra J. Lee, ScD; Xuelin Huang, PhD; Jeanne S. Mandelblatt, MD, MPH; Donald A. Berry, PhD; Allison W. Kurian, MD, MSc; Sylvia K. Plevritis, PhD JAMA

Question   How do associations between county-level age-adjusted breast cancer mortality and population demographic, environmental, lifestyle, and health care access characteristics vary geographically in the US?

Findings   This cross-sectional study of 2176 US counties found that the statistically significant positive association between obesity and breast cancer mortality was consistent across all counties in the US, but that access to factors in the built environment to support a healthy lifestyle had varying associations with mortality based on the county in which an individual lives.

Meaning   These results suggest that breast cancer mortality in the US can be affected by where individuals live, and that more comprehensive and geographically targeted interventions may lead to healthier communities.

Importance   Breast cancer mortality is complex and traditional approaches that seek to identify determinants of mortality assume that their effects on mortality are stationary across geographic space and scales.

Objective   To identify geographic variation in the associations of population demographics, environmental, lifestyle, and health care access with breast cancer mortality at the US county-level.

Design, Setting, and Participants   This geospatial cross-sectional study used data from the Surveillance, Epidemiology, and End Results (SEER) database on adult female patients with breast cancer. Statistical and spatial analysis was completed using adjusted mortality rates from 2015 to 2019 for 2176 counties in the US. Data were analyzed July 2022.

Exposures   County-level population demographics, environmental, lifestyle, and health care access variables were obtained from open data sources.

Main Outcomes and Measures   Model coefficients describing the association between 18 variables and age-adjusted breast cancer mortality rate. Compared with a multivariable linear regression (OLS), multiscale geographically weighted regression (MGWR) relaxed the assumption of spatial stationarity and allowed for the magnitude, direction, and significance of coefficients to change across geographic space.

Results   Both OLS and MGWR models agreed that county-level age-adjusted breast cancer mortality rates were significantly positively associated with obesity (OLS: β, 1.21; 95% CI, 0.88 to 1.54; mean [SD] MGWR: β, 0.72 [0.02]) and negatively associated with proportion of adults screened via mammograms (OLS: β, −1.27; 95% CI, −1.70 to −0.84; mean [SD] MGWR: β, −1.07 [0.16]). Furthermore, the MGWR model revealed that these 2 determinants were associated with a stationary effect on mortality across the US. However, the MGWR model provided important insights on other county-level factors differentially associated with breast cancer mortality across the US. Both models agreed that smoking (OLS: β, −0.65; 95% CI, −0.98 to −0.32; mean [SD] MGWR: β, −0.75 [0.92]), food environment index (OLS: β, −1.35; 95% CI, −1.72 to −0.98; mean [SD] MGWR: β, −1.69 [0.70]), exercise opportunities (OLS: β, −0.56; 95% CI, −0.91 to −0.21; mean [SD] MGWR: β, −0.59 [0.81]), racial segregation (OLS: β, −0.60; 95% CI, −0.89 to −0.31; mean [SD] MGWR: β, −0.47 [0.41]), mental health care physician ratio (OLS: β, −0.93; 95% CI, −1.44 to −0.42; mean [SD] MGWR: β, −0.48 [0.92]), and primary care physician ratio (OLS: β, −1.46; 95% CI, −2.13 to −0.79; mean [SD] MGWR: β, −1.06 [0.57]) were negatively associated with breast cancer mortality, and that light pollution was positively associated (OLS: β, 0.48; 95% CI, 0.24 to 0.72; mean [SD] MGWR: β, 0.27 [0.04]). But in the MGWR model, the magnitude of effect sizes and significance varied across geographical regions. Inversely, the OLS model found that disability was not a significant variable for breast cancer mortality, yet the MGWR model found that it was significantly positively associated in some geographical locations.

Conclusions and Relevance   This cross-sectional study found that not all social determinants associated with breast cancer mortality are spatially stationary and provides spatially explicit insights for public health practitioners to guide geographically targeted interventions.

Breast cancer is the leading cause of cancer-related deaths among women in the US. 1 Biological and behavioral determinants of breast cancer mortality are generally known and have guided successful interventions and prevention programs that target individuals at risk. 2 - 4 However, due to the complex interrelation between individual and contextual determinants, geographic disparities in breast cancer mortality remain difficult to address. 5 , 6

While traditional regression approaches, commonly used in urban health research, have been useful in identifying determinants of breast cancer mortality, they are limited in that they assume spatial stationarity, meaning that one measure is used to describe the association between the independent and response variable for the entire area under study. Toward addressing this assumption, spatial approaches such as geographically weighted regression (GWR) 7 and geographical random forest (GRF) 8 compute local associations or the relative importance of variables and breast cancer mortality for each geographic unit within the study area. 9 - 11 However, these approaches disregard the possibility that variables affecting breast cancer likely manifest at different spatial scales. For example, on a smaller scale, neighborhoods may have varying degrees of access to exercise opportunities. On a larger scale, states may fund different programs that support remission care for uninsured individuals.

The spatial heterogeneity of breast cancer mortality across the US ( Figure 1 A) presents an opportunity to explore the contextual and environmental variables that might give rise to such spatial disparities and the potential for nonstationarity in these data across space and scales. One such approach, multiscale geographically weighted regression (MGWR), is an extension of GWR that allows for the association between determinants and breast cancer mortality to vary both across geographic space and at different scales. 12 Therefore, the objective of this geospatial cross-sectional study is to identify county-level social determinants of health including population demographics, environment, lifestyle, health care access, and pollutant variables using MGWR to address both spatial heterogeneity and the effects of scale on breast cancer mortality. We focus primarily on age-adjusted female breast cancer mortality as our dependent variable, which normalizes county mortality rates based on age characteristics of the corresponding county using 2000 US census data. 13 , 14 The goal of this study is to enable location-specific interventions that can be addressed at various levels of public health.

For each US county, excluding Alaska and Hawaii, age-adjusted female breast cancer mortality rates from 2015 to 2019 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database version 8.4.0.1 15 in June of 2022 ( Figure 1 A). Female breast cancer mortality rate is defined as the number of deaths per 100 000 women per year and age-adjusted rates are standardized to the 2000 US population. 14 Approximately one-third of the 3108 counties that make up the contiguous US (932 counties for results on age-adjusted rates) had no reported data, which is standard practice for counties reporting less than 10 deaths from 2015 to 2019. Thus, these counties were excluded from the analysis. Since all data were publicly available and deidentified, neither informed consent nor institutional review board approval was required. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline for cross-sectional studies.

County-level data were retrieved for 57 social determinants selected a priori for their known association with breast cancer incidence and mortality (eTable 1 in Supplement 1 ). Independent variables were collected from the Social Vulnerability Index, 16 County Health Rankings & Roadmaps, 17 OpenStreetMap, 18 raw points of interest data from SafeGraph, NASA Black Marble, 19 the US National Land Cover Data set, 20 , 21 and ClinicalTrials.gov (eAppendix in Supplement 1 ). 22 Variables were subclassified into 5 main categories: access to health care (7 variables), sociodemographics of the population (24 variables), lifestyle (5 variables), physical environment (15 variables), and pollutant (6 variables).

Of the 57 total variables, 24 variables were removed due to collinearity ( r  > 0.6 or variance inflation factor above 3.0) (eTable 1 and eFigure 1 in Supplement 1 ). 23 The remaining 33 variables were evaluated using a leaps algorithm 24 in R version 2.2.1 (R Project for Statistical Computing) to determine the best subset of variables. When using age-adjusted female breast cancer mortality, the final variable set contained 18 variables ( Table 1 and Table 2 ). We note that none of the variables from the pollutant category were selected in the final model due to poor predictive capability. Some variables were log transformed to improve model convergence. All variables were scaled to have a mean of zero with an SD of 1.

To better visualize the spatial patterns of breast cancer mortality across the US, a cluster and outlier analysis of the age-adjusted breast cancer mortality rates were computed using a Local Moran I approach. 25 Next, excluding counties with missing data, a linear regression model (OLS) was fit to the county-level data in which all variables were regressed against age-adjusted female breast cancer mortality. Linear models assume that a variable’s magnitude of effect is constant across the sample space.

To assess whether the effects of our independent variables vary geographically across the US, an MGWR model was also computed using the same variable sets. Unlike a linear model, MGWR allows the strength and direction of effect to vary across the sample space—potentially revealing county-specific variation in trends. 12 Formally, MGWR computes a local regression model for every county ( i ) in the data set by borrowing data from other surrounding counties ( j ) that fall within county i ’s neighborhood. The number of nearest neighbors from which data will be borrowed (that comprise j ) is referred to as the bandwidth. MGWR recognizes that not all relationships occur at the same spatial scale. Thus, the bandwidth size varies for each variable, based on an optimization algorithm.

The MGWR model is expressed as:

where β bwj is the estimation of the coefficient for county i and bwj is the optimal bandwidth size. The resulting bandwidths provide important information on the scale at which certain processes occur, thus indicating spatial nonstationarity. Smaller bandwidths indicate more local variation. Whereas larger values indicate a more global response similar to OLS. All statistical and spatial analysis were computed in ArcGIS Pro version 3.1.0 (Esri). Statistical significance was determined by 95% CIs. See eMethods in Supplement 1 for additional theoretical and technical details of the analyses.

The Local Moran I analysis identified spatial clusters and outliers of counties based on their age-adjusted breast cancer mortality rates ( Figure 1 B). A belt of counties with high breast cancer mortality rates (high-high cluster) was found to stretch from Kansas through Oklahoma east to Arkansas, Louisiana, Mississippi, Alabama, and Georgia and then up through South and North Carolina to Virginia. Another high-high cluster was observed along the borders of Kentucky, West Virginia, and Ohio. In contrast, clusters of counties with low breast cancer mortality rates (low-low cluster) were observed in California, Arizona, much of the Northeast, and parts of the Midwest. The map also highlights counties that have statistically high or low breast cancer mortality rates relative to their spatial neighbors (low-high outlier, high-low outlier). For example, Buffalo County, New York, has a much higher breast cancer mortality rate than the surrounding counties. In another example, Madison County, Tennessee, has a much lower breast cancer mortality rate than the surrounding counties.

We attempt to explain these spatial patterns of breast cancer mortality by comparing the coefficient of determination between risk factors and mortality rates using a conventional linear regression and the MGWR model. The MGWR was better at explaining the association between independent variables and breast cancer mortality rates across the US for adjusted mortality rates. For example, the linear model for age-adjusted female breast cancer mortality rates yielded an adjusted R 2  = 0.17 compared with an adjusted R 2  = 0.28 for the MGWR model ( Tables 1 and 2 , respectively).

A positive, statistically significant association between obesity and breast cancer mortality was observed in both the OLS (β, 1.21; 95% CI, 0.88 to 1.54; P  < .001) and the MGWR (mean [SD] β, 0.72 [0.02]). Similarly, a negative and statistically significant association between the proportion of adults screened with mammograms and breast cancer mortality was observed in the OLS (β, −1.27; 95% CI, −1.70 to −0.84; P  < .001) and the MGWR (mean [SD] β, −1.07 [0.16]). Furthermore, given that there are only small changes in the coefficients for obesity ( Figure 2 A) and proportion of adults screened for mammograms ( Figure 2 B), the MGWR results indicate that that the effects of these variables on mortality are spatially stationary.

The OLS and MGWR model agreed that in general breast cancer mortality was significantly negatively associated with smoking (OLS: β, −0.65; 95% CI, −0.98 to −0.32; P  < .001; mean [SD] MGWR β, −0.75 [0.92]), food environment index (OLS: β, −1.35; 95% CI, −1.72 to −0.98]; P  < .001; mean [SD] MGWR: β, −1.69 [0.70]), exercise opportunities (OLS: β, −0.56; 95% CI, −0.91 to −0.21; P  = .002; mean [SD] MGWR: β, −0.59 [0.81]), segregation (OLS: β, −0.60; 95% CI, −0.89 to −0.31; P  < .001; mean [SD] MGWR: β, −0.47 [0.41]), mental health care physician ratio (OLS: β, −0.93; 95% CI, −1.44 to −0.42; P  < .001; mean [SD] MGWR: β, −0.48 [0.92]), and primary care physician ratio (OLS: β, −1.46; 95% CI, −2.13 to −0.79; P  < .001; mean [SD] MGWR: β, −1.06 [0.57]), while positively associated with light pollution (mean radiance) (OLS: β, 0.48; 95% CI, 0.24 to 0.72; P  < .001; mean [SD] MGWR: β, 0.27 [0.04]) ( Tables 1 and 2 ).

However, while the OLS found that these variables are significant factors associated with breast cancer mortality overall, MGWR showed that they are only significant in some geographical locations. For example, where obesity and mammogram testing have a significant association with mortality in 100% of US counties, smoking had a significant effect in only 16.3%, food environment index in 80.3%, segregation in 22.6%, mental health care physician ratio in 14.0%, primary care physician ratio in 40.6%, and light pollution in 42.4%. Furthermore, the MGWR revealed that the magnitude of effect size of these variables varied from county to county, as demonstrated by the larger standard deviation of the beta coefficients and the smaller bandwidth sizes for these variables ( Table 2 ). Thus, the association between these variables and breast cancer mortality can be considered spatially nonstationary with effects that vary regionally in scale. For example, the food environment index was not significantly associated with breast cancer mortality in the western US ( Figure 3 A). Yet, in most of the southern and eastern US, the food environment index was positively associated with breast cancer mortality with coefficients ranging from −1.55 to −2.85. This association had the largest effect sizes (ranging from β = −2.36 to β = −2.85) in Louisiana, Mississippi, Arkansas, and Alabama as well as North Carolina and parts of South Carolina and Virginia. Additionally, where access to exercise opportunities and breast cancer mortality was not significant for most of the US, a positive association with coefficients ranging from −1.30 to −3.46 was found in central US and Florida ( Figure 3 B).

Finally, where OLS estimated that disability was not significant, the MGWR estimated that it was significant in 45% of counties and that on average it was positively associated with breast cancer mortality (mean [SD] MGWR β, 0.4 [0.17]). In contrast, where OLS found a negative association between the uninsured and breast cancer mortality (β, −0.32; 95% CI, −0.61 to −0.03; P  = .03), the MGWR found that the coefficients for this variable were not statistically significant for any county in the US. The 2 models agreed that unemployment, long commute, income inequality, number of hospitals, and proportion of natural land were not significantly associated with breast cancer mortality at the county level, with MGWR results not significant for 100% of counties. The methodology was also applied using unadjusted breast cancer mortality rates (2015-2019) as an outcome for comparison. The findings are consistent across both adjusted and unadjusted breast cancer mortality rates (eMethods in Supplement 1 ).

To our knowledge, this is the first study applying an MGWR model to assess how associations between breast cancer mortality and county-level social determinants vary across space and scale in the US. Based on the SEER age-adjusted rates collected between 2015 and 2019, breast cancer–associated mortality rates differed considerably across the US ( Figure 1 A and B). Alabama is a clear example of the diverse outcomes experienced by breast cancer patients based on their geographic location even under unified state programs. While the northern part of the state showed significant variation in age-adjusted mortality rates between counties, the southern part of the state displayed more homogeneous rates.

While the MGWR was better at explaining age-adjusted breast cancer mortality in general, both models showed a significant negative and spatially stationary association between breast cancer mortality and access to mammogram screening. Similarly, county-level obesity emerged as a variable with a positive association with breast cancer mortality that had a stationary effect across the US, but that the association had slightly higher effect sizes in the Southern states. Association between obesity and breast cancer incidence and mortality have been thoroughly examined in epidemiological, clinical, and preclinical studies. 26 - 29

Of interest, lifestyle factors that affect obesity, like the food environment index and exercise opportunities were also negatively associated with breast cancer mortality in the OLS and MGWR models. However, their effects were spatially nonstationary with regional-scale variation ( Figure 3 ). For example, food environment index, a variable that combines both physical and financial access to healthy foods, effect sizes for the association with reduced mortality were especially pronounced in areas that have previously been reported as cancer hot spots for non-Hispanic Black women, 30 such as areas along the Mississippi river, rural southern Virginia, and North Carolina ( Figure 1 B). Thus, our results indicate that more comprehensive and geographically targeted public health programs with a combined approach that seeks to both increase access to healthy and nutritional foods in underserved areas 31 and modify eating habits 32 - 34 could support filling the cancer disparity gap in this region. This highlights the importance of considering spatial nonstationarity of cancer mortality rates.

Access to physical exercise opportunities also emerged as a nonstationary risk factor associated with breast cancer mortality ( Figure 3 ). The beneficial effect of exercise and physical activity have been thoroughly described in the context of breast cancer incidence and mortality, including in individuals harboring genomic alterations of the BRCA1 and BRCA2 genes. 35 - 40 Meta-analyses have provided suggestive evidence that links availability of and engagement in physical activity with improved outcome for breast cancer patients. 41 - 43 Our MGWR model results indicated that access to exercise opportunities has a positive impact on breast cancer survivorship in areas highly populated by Latino and indigenous Native American communities, like New Mexico, Texas, and Florida, and at the 4 corners between New Mexico, Colorado, and Arizona. Understanding the effects of physical activity on breast cancer mortality in women of different ethnic background may open new opportunities for developing culturally specific educational programs. 44 - 47

While numerous studies have assessed social determinants of breast cancer mortality, most previous analyses were either limited to specific geographic areas or were conducted under the assumption that mortality determinants are spatially stationary. Our analysis provides unique insights on the spatial and scale-dependent relationship between health determinants and breast cancer mortality.

Because breast cancer death rates are relatively rare events in the general population, a few limitations of this study need to be addressed. While the SEER database remains the most reliable and comprehensive source of cancer-related mortality data across the US, to protect patients’ confidentiality, mortality rates are not reported for less populated areas where death totals do not reach the minimum reporting threshold. While we tested several approaches for imputing missing data, we found that imputation risked inflating mortality rates in counties with small populations or decreased the spatial variance that is observed in the nonimputed data. Thus, our analysis is biased toward counties that have 10 or more deaths in 5 years and may affect our findings.

In addition, most variables included in our analysis were measured at the county level, not specifically in women at risk for or affected by breast cancer, which may have affected our estimates. We also note that our final MGWR produces a moderate coefficient of determination, especially using the age-adjusted mortality rates as a dependent variable. This is likely due to the complexity of breast cancer mortality and determinants, producing variation that is difficult to capture in models. This is reflected in similar studies that use county-level data that also report moderate model performance, 10 , 11 but in general, especially when using individual level data, studies often choose not to report it at all.

Even with these limitations, the MGWR model demonstrated that factors known to be associated with breast cancer have heterogenous effects across geographic regions. By accounting for the inherent spatial distribution of risk factors, population diversity, and their effect on mortality, the MGWR model provides unique opportunities for identifying trends and conceiving policies and health interventions that target specific population characteristics.

In this cross-sectional study, we found county-level age-adjusted breast cancer mortality rates were significantly positively associated with obesity and negatively associated with proportion of adults screened via mammograms, and that this association was spatially stationary. Smoking, food environment index, exercise opportunities, segregation, mental health care physician ratio, and primary care physician ratio were negatively associated with breast cancer mortality, and light pollution was positively associated. However, the MGWR revealed that the magnitude of effect and significance of these variables varied across geographical regions.

Devising new approaches to address health disparities is a growing priority in cancer research. It is well known that health disparities are driven by complex and often interrelated factors. Untangling these complex relationships requires innovative and multidisciplinary approaches able to tie place-specific factors with disease-related outcomes. The MGWR approach proposed brought a novel perspective for capturing the spatial interrelations between individuals and contextual factors on a large geographic scale. As suggested by our analysis, this approach may have an unparalleled ability to identify vulnerable populations and geographic areas where targeted interventions may lead to healthier communities.

Accepted for Publication: August 8, 2023.

Published: September 14, 2023. doi:10.1001/jamanetworkopen.2023.33618

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Anderson T et al. JAMA Network Open .

Corresponding Author: Taylor Anderson, PhD, Department of Geography and Geoinformation Science, George Mason University, 4400 University Dr, Fairfax, VA 22030 ( [email protected] ).

Author Contributions: Dr Anderson had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Anderson, Kokkinakis, Dao, Gallo, Pierobon.

Acquisition, analysis, or interpretation of data: Anderson, Herrera, Mireku, Barner, Kokkinakis, Webber, Merida, Gallo, Pierobon.

Drafting of the manuscript: Anderson, Mireku, Barner, Kokkinakis, Webber, Gallo, Pierobon.

Critical review of the manuscript for important intellectual content: Anderson, Herrera, Barner, Kokkinakis, Dao, Webber, Merida, Gallo, Pierobon.

Statistical analysis: Anderson, Herrera, Mireku, Barner, Kokkinakis, Dao, Webber, Gallo.

Obtained funding: Anderson, Gallo.

Administrative, technical, or material support: Anderson, Kokkinakis, Webber, Merida, Gallo.

Supervision: Anderson, Gallo, Pierobon.

Conflict of Interest Disclosures: Dr Pierobon reported receiving royalties and consulting fees from TheraLink Technologies outside the submitted work; she reported authoring patents and patent applications assigned to George Mason University outside the submitted work, for which she may receive royalties. No other disclosures were reported.

Funding/Support: This research was funded by Office of Student Scholarship, Creative Activities and Research (OSCAR) at George Mason University.

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: We thank the Surveillance, Epidemiology, and End Results staff for all their help and support.

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First study to measure toxic metals in tampons shows arsenic and lead, among other contaminants

• By Elise Proulx
• 3 min. read ▪ Published July 3
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Tampons from several brands that potentially millions of people use each month can contain toxic metals like lead, arsenic, and cadmium, a new study led by a UC Berkeley researcher has found.

Tampons are of particular concern as a potential source of exposure to chemicals, including metals, because the skin of the vagina has a higher potential for chemical absorption than skin elsewhere on the body. In addition, the products are used by a large percentage of the population on a monthly basis—50–80% of those who menstruate use tampons—for several hours at a time.

“Despite this large potential for public health concern, very little research has been done to measure chemicals in tampons,” said lead author Jenni A. Shearston , a postdoctoral scholar at the UC Berkeley School of Public Health and UC Berkeley’s Department of Environmental Science, Policy, & Management. “To our knowledge, this is the first paper to measure metals in tampons. Concerningly, we found concentrations of all metals we tested for, including toxic metals like arsenic and lead.”

Metals have been found to increase the risk of dementia, infertility, diabetes, and cancer. They can damage the liver, kidneys, and brain, as well as the cardiovascular, nervous, and endocrine systems. In addition, metals can harm maternal health and fetal development.

“Although toxic metals are ubiquitous and we are exposed to low levels at any given time, our study clearly shows that metals are also present in menstrual products, and that women might be at higher risk for exposure using these products,” said study co-author Kathrin Schilling , assistant professor at Columbia University Mailman School of Public Health.

Researchers evaluated levels of 16 metals (arsenic, barium, calcium, cadmium, cobalt, chromium, copper, iron, manganese, mercury, nickel, lead, selenium, strontium, vanadium, and zinc) in 30 tampons from 14 different brands. The metal concentrations varied by where the tampons were purchased (US vs. EU/UK), organic vs. non-organic, and store- vs. name-brand. However, they found that metals were present in all types of tampons; no category had consistently lower concentrations of all or most metals. Lead concentrations were higher in non-organic tampons but arsenic was higher in organic tampons.

Metals could make their way into tampons a number of ways: The cotton material could have absorbed the metals from water, air, soil, through a nearby contaminant (for example, if a cotton field was near a lead smelter), or some might be added intentionally during manufacturing as part of a pigment, whitener, antibacterial agent, or some other process in the factory producing the products.

“I really hope that manufacturers are required to test their products for metals, especially for toxic metals,” said Shearston. “It would be exciting to see the public call for this, or to ask for better labeling on tampons and other menstrual products.”

For the moment, it’s unclear if the metals detected by this study are contributing to any negative health effects. Future research will test how much of these metals can leach out of the tampons and be absorbed by the body; as well as measuring the presence of other chemicals in tampons.

Additional authors include: Kristen Upson of the College of Human Medicine, Michigan State University; Milo Gordon, Vivian Do, Olgica Balac, and Marianthi-Anna Kioumourtzoglou of Columbia University Mailman School of Public Health; and Khue Nguyen and Beizhan Yan of Lamont-Doherty Earth Observatory of Columbia University.

Funding was provided by the National Institute of Environmental Health Sciences; the National Heart, Lung, and Blood Institute; and the National Institute of Nursing Research.

In the Media:

• Lead, Arsenic, Other Toxic Metals Found in Dozens of Tampon Products – Los Angeles Magazine
• Lead and other toxic metals found in tampons, study finds – The Atlanta Journal-Constitution
• Toxic Metal in Tampons Risks Brain’s Cognitive Function, Scientists Warn – Newsweek
• New study finds lead and arsenic in tampons. But don’t panic, experts say – TODAY
• Tampons contain toxic metals such as lead and arsenic, UC Berkeley study finds – San Francisco Chronicle
• Toxic Tampon Warning As Arsenic and Lead Found in Common Menstrual Products – Newsweek
• Some tampons found to contain LEAD and other toxic metals that could be absorbed into the body, alarming study suggests – Daily Mail

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