Why people are becoming addicted to social media: A qualitative study

Affiliations.

  • 1 Social Determinants of Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
  • 2 Department of Biostatistics and Epidemiology, School of Public Health, Kerman University of Medical Sciences, Kerman, Iran.
  • 3 Nursing Research Center, Razi Faculty of Nursing and Midwifery, Department of Critical Care Nursing, Kerman University of Medical Sciences, Kerman, Iran.
  • 4 Neuroscience Research Center, Institute of Neuropharmacology, Shahid Beheshti Hospital, Kerman University of Medical Sciences, Kerman, Iran.
  • 5 Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran.
  • 6 Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
  • PMID: 34250109
  • PMCID: PMC8249956
  • DOI: 10.4103/jehp.jehp_1109_20

Background: Social media addiction (SMA) led to the formation of health-threatening behaviors that can have a negative impact on the quality of life and well-being. Many factors can develop an exaggerated tendency to use social media (SM), which can be prevented in most cases. This study aimed to explore the reasons for SMA.

Materials and methods: This qualitative study was conducted using content analysis. A total of 18 SM addicted subjects were included through purposive sampling. Data were collected through semi-structured interviews and analyzed using the Lundman and Graneheim qualitative content analysis method. A total of 18 SM addicted subjects were included through purposive sampling. Data were collected through semi-structured interviews and analyzed using the Lundman and Graneheim qualitative content analysis method.

Results: The main category of "weakness in acquiring life skills" was extracted with three themes: "problems in socializing" (including communicating and escaping loneliness), "problems in resiliency" (including devastation in harsh conditions and inability to recover oneself and "lack of problem-solving skills" (including weaknesses in analysis and decision making and disorganization in planning).

Conclusions: Weakness in life skills plays an important role in individuals' addiction to SM and formation of the health-threatening behaviors. Since SMA can affect behavioral health, policymakers must adopt educational and preventive programs to increase the knowledge and skills of individuals in different societies in the modern world.

Keywords: Addiction; Iran; qualitative study; social media.

Copyright: © 2021 Journal of Education and Health Promotion.

SYSTEMATIC REVIEW article

Research trends in social media addiction and problematic social media use: a bibliometric analysis.

\nAlfonso Pellegrino

  • 1 Sasin School of Management, Chulalongkorn University, Bangkok, Thailand
  • 2 Business Administration Division, Mahidol University International College, Mahidol University, Nakhon Pathom, Thailand

Despite their increasing ubiquity in people's lives and incredible advantages in instantly interacting with others, social media's impact on subjective well-being is a source of concern worldwide and calls for up-to-date investigations of the role social media plays in mental health. Much research has discovered how habitual social media use may lead to addiction and negatively affect adolescents' school performance, social behavior, and interpersonal relationships. The present study was conducted to review the extant literature in the domain of social media and analyze global research productivity during 2013–2022. Bibliometric analysis was conducted on 501 articles that were extracted from the Scopus database using the keywords social media addiction and problematic social media use. The data were then uploaded to VOSviewer software to analyze citations, co-citations, and keyword co-occurrences. Volume, growth trajectory, geographic distribution of the literature, influential authors, intellectual structure of the literature, and the most prolific publishing sources were analyzed. The bibliometric analysis presented in this paper shows that the US, the UK, and Turkey accounted for 47% of the publications in this field. Most of the studies used quantitative methods in analyzing data and therefore aimed at testing relationships between variables. In addition, the findings in this study show that most analysis were cross-sectional. Studies were performed on undergraduate students between the ages of 19–25 on the use of two social media platforms: Facebook and Instagram. Limitations as well as research directions for future studies are also discussed.

Introduction

Social media generally refers to third-party internet-based platforms that mainly focus on social interactions, community-based inputs, and content sharing among its community of users and only feature content created by their users and not that licensed from third parties ( 1 ). Social networking sites such as Facebook, Instagram, and TikTok are prominent examples of social media that allow people to stay connected in an online world regardless of geographical distance or other obstacles ( 2 , 3 ). Recent evidence suggests that social networking sites have become increasingly popular among adolescents following the strict policies implemented by many countries to counter the COVID-19 pandemic, including social distancing, “lockdowns,” and quarantine measures ( 4 ). In this new context, social media have become an essential part of everyday life, especially for children and adolescents ( 5 ). For them such media are a means of socialization that connect people together. Interestingly, social media are not only used for social communication and entertainment purposes but also for sharing opinions, learning new things, building business networks, and initiate collaborative projects ( 6 ).

Among the 7.91 billion people in the world as of 2022, 4.62 billion active social media users, and the average time individuals spent using the internet was 6 h 58 min per day with an average use of social media platforms of 2 h and 27 min ( 7 ). Despite their increasing ubiquity in people's lives and the incredible advantages they offer to instantly interact with people, an increasing number of studies have linked social media use to negative mental health consequences, such as suicidality, loneliness, and anxiety ( 8 ). Numerous sources have expressed widespread concern about the effects of social media on mental health. A 2011 report by the American Academy of Pediatrics (AAP) identifies a phenomenon known as Facebook depression which may be triggered “when preteens and teens spend a great deal of time on social media sites, such as Facebook, and then begin to exhibit classic symptoms of depression” ( 9 ). Similarly, the UK's Royal Society for Public Health (RSPH) claims that there is a clear evidence of the relationship between social media use and mental health issues based on a survey of nearly 1,500 people between the ages of 14–24 ( 10 ). According to some authors, the increase in usage frequency of social media significantly increases the risks of clinical disorders described (and diagnosed) as “Facebook depression,” “fear of missing out” (FOMO), and “social comparison orientation” (SCO) ( 11 ). Other risks include sexting ( 12 ), social media stalking ( 13 ), cyber-bullying ( 14 ), privacy breaches ( 15 ), and improper use of technology. Therefore, social media's impact on subjective well-being is a source of concern worldwide and calls for up-to-date investigations of the role social media plays with regard to mental health ( 8 ). Many studies have found that habitual social media use may lead to addiction and thus negatively affect adolescents' school performance, social behavior, and interpersonal relationships ( 16 – 18 ). As a result of addiction, the user becomes highly engaged with online activities motivated by an uncontrollable desire to browse through social media pages and “devoting so much time and effort to it that it impairs other important life areas” ( 19 ).

Given these considerations, the present study was conducted to review the extant literature in the domain of social media and analyze global research productivity during 2013–2022. The study presents a bibliometric overview of the leading trends with particular regard to “social media addiction” and “problematic social media use.” This is valuable as it allows for a comprehensive overview of the current state of this field of research, as well as identifies any patterns or trends that may be present. Additionally, it provides information on the geographical distribution and prolific authors in this area, which may help to inform future research endeavors.

In terms of bibliometric analysis of social media addiction research, few studies have attempted to review the existing literature in the domain extensively. Most previous bibliometric studies on social media addiction and problematic use have focused mainly on one type of screen time activity such as digital gaming or texting ( 20 ) and have been conducted with a focus on a single platform such as Facebook, Instagram, or Snapchat ( 21 , 22 ). The present study adopts a more comprehensive approach by including all social media platforms and all types of screen time activities in its analysis.

Additionally, this review aims to highlight the major themes around which the research has evolved to date and draws some guidance for future research directions. In order to meet these objectives, this work is oriented toward answering the following research questions:

(1) What is the current status of research focusing on social media addiction?

(2) What are the key thematic areas in social media addiction and problematic use research?

(3) What is the intellectual structure of social media addiction as represented in the academic literature?

(4) What are the key findings of social media addiction and problematic social media research?

(5) What possible future research gaps can be identified in the field of social media addiction?

These research questions will be answered using bibliometric analysis of the literature on social media addiction and problematic use. This will allow for an overview of the research that has been conducted in this area, including information on the most influential authors, journals, countries of publication, and subject areas of study. Part 2 of the study will provide an examination of the intellectual structure of the extant literature in social media addiction while Part 3 will discuss the research methodology of the paper. Part 4 will discuss the findings of the study followed by a discussion under Part 5 of the paper. Finally, in Part 7, gaps in current knowledge about this field of research will be identified.

Literature review

Social media addiction research context.

Previous studies on behavioral addictions have looked at a lot of different factors that affect social media addiction focusing on personality traits. Although there is some inconsistency in the literature, numerous studies have focused on three main personality traits that may be associated with social media addiction, namely anxiety, depression, and extraversion ( 23 , 24 ).

It has been found that extraversion scores are strongly associated with increased use of social media and addiction to it ( 25 , 26 ). People with social anxiety as well as people who have psychiatric disorders often find online interactions extremely appealing ( 27 ). The available literature also reveals that the use of social media is positively associated with being female, single, and having attention deficit hyperactivity disorder (ADHD), obsessive compulsive disorder (OCD), or anxiety ( 28 ).

In a study by Seidman ( 29 ), the Big Five personality traits were assessed using Saucier's ( 30 ) Mini-Markers Scale. Results indicated that neurotic individuals use social media as a safe place for expressing their personality and meet belongingness needs. People affected by neurosis tend to use online social media to stay in touch with other people and feel better about their social lives ( 31 ). Narcissism is another factor that has been examined extensively when it comes to social media, and it has been found that people who are narcissistic are more likely to become addicted to social media ( 32 ). In this case users want to be seen and get “likes” from lots of other users. Longstreet and Brooks ( 33 ) did a study on how life satisfaction depends on how much money people make. Life satisfaction was found to be negatively linked to social media addiction, according to the results. When social media addiction decreases, the level of life satisfaction rises. But results show that in lieu of true-life satisfaction people use social media as a substitute (for temporary pleasure vs. longer term happiness).

Researchers have discovered similar patterns in students who tend to rank high in shyness: they find it easier to express themselves online rather than in person ( 34 , 35 ). With the use of social media, shy individuals have the opportunity to foster better quality relationships since many of their anxiety-related concerns (e.g., social avoidance and fear of social devaluation) are significantly reduced ( 36 , 37 ).

Problematic use of social media

The amount of research on problematic use of social media has dramatically increased since the last decade. But using social media in an unhealthy manner may not be considered an addiction or a disorder as this behavior has not yet been formally categorized as such ( 38 ). Although research has shown that people who use social media in a negative way often report negative health-related conditions, most of the data that have led to such results and conclusions comprise self-reported data ( 39 ). The dimensions of excessive social media usage are not exactly known because there are not enough diagnostic criteria and not enough high-quality long-term studies available yet. This is what Zendle and Bowden-Jones ( 40 ) noted in their own research. And this is why terms like “problematic social media use” have been used to describe people who use social media in a negative way. Furthermore, if a lot of time is spent on social media, it can be hard to figure out just when it is being used in a harmful way. For instance, people easily compare their appearance to what they see on social media, and this might lead to low self-esteem if they feel they do not look as good as the people they are following. According to research in this domain, the extent to which an individual engages in photo-related activities (e.g., taking selfies, editing photos, checking other people's photos) on social media is associated with negative body image concerns. Through curated online images of peers, adolescents face challenges to their self-esteem and sense of self-worth and are increasingly isolated from face-to-face interaction.

To address this problem the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) has been used by some scholars ( 41 , 42 ). These scholars have used criteria from the DSM-V to describe one problematic social media use, internet gaming disorder, but such criteria could also be used to describe other types of social media disorders. Franchina et al. ( 43 ) and Scott and Woods ( 44 ), for example, focus their attention on individual-level factors (like fear of missing out) and family-level factors (like childhood abuse) that have been used to explain why people use social media in a harmful way. Friends-level factors have also been explored as a social well-being measurement to explain why people use social media in a malevolent way and demonstrated significant positive correlations with lower levels of friend support ( 45 ). Macro-level factors have also been suggested, such as the normalization of surveillance ( 46 ) and the ability to see what people are doing online ( 47 ). Gender and age seem to be highly associated to the ways people use social media negatively. Particularly among girls, social media use is consistently associated with mental health issues ( 41 , 48 , 49 ), an association more common among older girls than younger girls ( 46 , 48 ).

Most studies have looked at the connection between social media use and its effects (such as social media addiction) and a number of different psychosomatic disorders. In a recent study conducted by Vannucci and Ohannessian ( 50 ), the use of social media appears to have a variety of effects “on psychosocial adjustment during early adolescence, with high social media use being the most problematic.” It has been found that people who use social media in a harmful way are more likely to be depressed, anxious, have low self-esteem, be more socially isolated, have poorer sleep quality, and have more body image dissatisfaction. Furthermore, harmful social media use has been associated with unhealthy lifestyle patterns (for example, not getting enough exercise or having trouble managing daily obligations) as well as life threatening behaviors such as illicit drug use, excessive alcohol consumption and unsafe sexual practices ( 51 , 52 ).

A growing body of research investigating social media use has revealed that the extensive use of social media platforms is correlated with a reduced performance on cognitive tasks and in mental effort ( 53 ). Overall, it appears that individuals who have a problematic relationship with social media or those who use social media more frequently are more likely to develop negative health conditions.

Social media addiction and problematic use systematic reviews

Previous studies have revealed the detrimental impacts of social media addiction on users' health. A systematic review by Khan and Khan ( 20 ) has pointed out that social media addiction has a negative impact on users' mental health. For example, social media addiction can lead to stress levels rise, loneliness, and sadness ( 54 ). Anxiety is another common mental health problem associated with social media addiction. Studies have found that young adolescents who are addicted to social media are more likely to suffer from anxiety than people who are not addicted to social media ( 55 ). In addition, social media addiction can also lead to physical health problems, such as obesity and carpal tunnel syndrome a result of spending too much time on the computer ( 22 ).

Apart from the negative impacts of social media addiction on users' mental and physical health, social media addiction can also lead to other problems. For example, social media addiction can lead to financial problems. A study by Sharif and Yeoh ( 56 ) has found that people who are addicted to social media tend to spend more money than those who are not addicted to social media. In addition, social media addiction can also lead to a decline in academic performance. Students who are addicted to social media are more likely to have lower grades than those who are not addicted to social media ( 57 ).

Research methodology

Bibliometric analysis.

Merigo et al. ( 58 ) use bibliometric analysis to examine, organize, and analyze a large body of literature from a quantitative, objective perspective in order to assess patterns of research and emerging trends in a certain field. A bibliometric methodology is used to identify the current state of the academic literature, advance research. and find objective information ( 59 ). This technique allows the researchers to examine previous scientific work, comprehend advancements in prior knowledge, and identify future study opportunities.

To achieve this objective and identify the research trends in social media addiction and problematic social media use, this study employs two bibliometric methodologies: performance analysis and science mapping. Performance analysis uses a series of bibliometric indicators (e.g., number of annual publications, document type, source type, journal impact factor, languages, subject area, h-index, and countries) and aims at evaluating groups of scientific actors on a particular topic of research. VOSviewer software ( 60 ) was used to carry out the science mapping. The software is used to visualize a particular body of literature and map the bibliographic material using the co-occurrence analysis of author, index keywords, nations, and fields of publication ( 61 , 62 ).

Data collection

After picking keywords, designing the search strings, and building up a database, the authors conducted a bibliometric literature search. Scopus was utilized to gather exploration data since it is a widely used database that contains the most comprehensive view of the world's research output and provides one of the most effective search engines. If the research was to be performed using other database such as Web Of Science or Google Scholar the authors may have obtained larger number of articles however they may not have been all particularly relevant as Scopus is known to have the most widest and most relevant scholar search engine in marketing and social science. A keyword search for “social media addiction” OR “problematic social media use” yielded 553 papers, which were downloaded from Scopus. The information was gathered in March 2022, and because the Scopus database is updated on a regular basis, the results may change in the future. Next, the authors examined the titles and abstracts to see whether they were relevant to the topics treated. There were two common grounds for document exclusion. First, while several documents emphasized the negative effects of addiction in relation to the internet and digital media, they did not focus on social networking sites specifically. Similarly, addiction and problematic consumption habits were discussed in relation to social media in several studies, although only in broad terms. This left a total of 511 documents. Articles were then limited only to journal articles, conference papers, reviews, books, and only those published in English. This process excluded 10 additional documents. Then, the relevance of the remaining articles was finally checked by reading the titles, abstracts, and keywords. Documents were excluded if social networking sites were only mentioned as a background topic or very generally. This resulted in a final selection of 501 research papers, which were then subjected to bibliometric analysis (see Figure 1 ).

www.frontiersin.org

Figure 1 . Preferred reporting items for systematic reviews and meta-analysis (PRISMA) flowchart showing the search procedures used in the review.

After identifying 501 Scopus files, bibliographic data related to these documents were imported into an Excel sheet where the authors' names, their affiliations, document titles, keywords, abstracts, and citation figures were analyzed. These were subsequently uploaded into VOSViewer software version 1.6.8 to begin the bibliometric review. Descriptive statistics were created to define the whole body of knowledge about social media addiction and problematic social media use. VOSViewer was used to analyze citation, co-citation, and keyword co-occurrences. According to Zupic and Cater ( 63 ), co-citation analysis measures the influence of documents, authors, and journals heavily cited and thus considered influential. Co-citation analysis has the objective of building similarities between authors, journals, and documents and is generally defined as the frequency with which two units are cited together within the reference list of a third article.

The implementation of social media addiction performance analysis was conducted according to the models recently introduced by Karjalainen et al. ( 64 ) and Pattnaik ( 65 ). Throughout the manuscript there are operational definitions of relevant terms and indicators following a standardized bibliometric approach. The cumulative academic impact (CAI) of the documents was measured by the number of times they have been cited in other scholarly works while the fine-grained academic impact (FIA) was computed according to the authors citation analysis and authors co-citation analysis within the reference lists of documents that have been specifically focused on social media addiction and problematic social media use.

Results of the study presented here include the findings on social media addiction and social media problematic use. The results are presented by the foci outlined in the study questions.

Volume, growth trajectory, and geographic distribution of the literature

After performing the Scopus-based investigation of the current literature regarding social media addiction and problematic use of social media, the authors obtained a knowledge base consisting of 501 documents comprising 455 journal articles, 27 conference papers, 15 articles reviews, 3 books and 1 conference review. The included literature was very recent. As shown in Figure 2 , publication rates started very slowly in 2013 but really took off in 2018, after which publications dramatically increased each year until a peak was reached in 2021 with 195 publications. Analyzing the literature published during the past decade reveals an exponential increase in scholarly production on social addiction and its problematic use. This might be due to the increasingly widespread introduction of social media sites in everyday life and the ubiquitous diffusion of mobile devices that have fundamentally impacted human behavior. The dip in the number of publications in 2022 is explained by the fact that by the time the review was carried out the year was not finished yet and therefore there are many articles still in press.

www.frontiersin.org

Figure 2 . Annual volume of social media addiction or social media problematic use ( n = 501).

The geographical distribution trends of scholarly publications on social media addiction or problematic use of social media are highlighted in Figure 3 . The articles were assigned to a certain country according to the nationality of the university with whom the first author was affiliated with. The figure shows that the most productive countries are the USA (92), the U.K. (79), and Turkey ( 63 ), which combined produced 236 articles, equal to 47% of the entire scholarly production examined in this bibliometric analysis. Turkey has slowly evolved in various ways with the growth of the internet and social media. Anglo-American scholarly publications on problematic social media consumer behavior represent the largest research output. Yet it is interesting to observe that social networking sites studies are attracting many researchers in Asian countries, particularly China. For many Chinese people, social networking sites are a valuable opportunity to involve people in political activism in addition to simply making purchases ( 66 ).

www.frontiersin.org

Figure 3 . Global dispersion of social networking sites in relation to social media addiction or social media problematic use.

Analysis of influential authors

This section analyses the high-impact authors in the Scopus-indexed knowledge base on social networking sites in relation to social media addiction or problematic use of social media. It provides valuable insights for establishing patterns of knowledge generation and dissemination of literature about social networking sites relating to addiction and problematic use.

Table 1 acknowledges the top 10 most highly cited authors with the highest total citations in the database.

www.frontiersin.org

Table 1 . Highly cited authors on social media addiction and problematic use ( n = 501).

Table 1 shows that MD Griffiths (sixty-five articles), CY Lin (twenty articles), and AH Pakpour (eighteen articles) are the most productive scholars according to the number of Scopus documents examined in the area of social media addiction and its problematic use . If the criteria are changed and authors ranked according to the overall number of citations received in order to determine high-impact authors, the same three authors turn out to be the most highly cited authors. It should be noted that these highly cited authors tend to enlist several disciplines in examining social media addiction and problematic use. Griffiths, for example, focuses on behavioral addiction stemming from not only digital media usage but also from gambling and video games. Lin, on the other hand, focuses on the negative effects that the internet and digital media can have on users' mental health, and Pakpour approaches the issue from a behavioral medicine perspective.

Intellectual structure of the literature

In this part of the paper, the authors illustrate the “intellectual structure” of the social media addiction and the problematic use of social media's literature. An author co-citation analysis (ACA) was performed which is displayed as a figure that depicts the relations between highly co-cited authors. The study of co-citation assumes that strongly co-cited authors carry some form of intellectual similarity ( 67 ). Figure 4 shows the author co-citation map. Nodes represent units of analysis (in this case scholars) and network ties represent similarity connections. Nodes are sized according to the number of co-citations received—the bigger the node, the more co-citations it has. Adjacent nodes are considered intellectually similar.

www.frontiersin.org

Figure 4 . Two clusters, representing the intellectual structure of the social media and its problematic use literature.

Scholars belonging to the green cluster (Mental Health and Digital Media Addiction) have extensively published on medical analysis tools and how these can be used to heal users suffering from addiction to digital media, which can range from gambling, to internet, to videogame addictions. Scholars in this school of thought focus on the negative effects on users' mental health, such as depression, anxiety, and personality disturbances. Such studies focus also on the role of screen use in the development of mental health problems and the increasing use of medical treatments to address addiction to digital media. They argue that addiction to digital media should be considered a mental health disorder and treatment options should be made available to users.

In contrast, scholars within the red cluster (Social Media Effects on Well Being and Cyberpsychology) have focused their attention on the effects of social media toward users' well-being and how social media change users' behavior, focusing particular attention on the human-machine interaction and how methods and models can help protect users' well-being. Two hundred and two authors belong to this group, the top co-cited being Andreassen (667 co-citations), Pallasen (555 co-citations), and Valkenburg (215 co-citations). These authors have extensively studied the development of addiction to social media, problem gambling, and internet addiction. They have also focused on the measurement of addiction to social media, cyberbullying, and the dark side of social media.

Most influential source title in the field of social media addiction and its problematic use

To find the preferred periodicals in the field of social media addiction and its problematic use, the authors have selected 501 articles published in 263 journals. Table 2 gives a ranked list of the top 10 journals that constitute the core publishing sources in the field of social media addiction research. In doing so, the authors analyzed the journal's impact factor, Scopus Cite Score, h-index, quartile ranking, and number of publications per year.

www.frontiersin.org

Table 2 . Top 10 most cited and more frequently mentioned documents in the field of social media addiction.

The journal Addictive Behaviors topped the list, with 700 citations and 22 publications (4.3%), followed by Computers in Human Behaviors , with 577 citations and 13 publications (2.5%), Journal of Behavioral Addictions , with 562 citations and 17 publications (3.3%), and International Journal of Mental Health and Addiction , with 502 citations and 26 publications (5.1%). Five of the 10 most productive journals in the field of social media addiction research are published by Elsevier (all Q1 rankings) while Springer and Frontiers Media published one journal each.

Documents citation analysis identified the most influential and most frequently mentioned documents in a certain scientific field. Andreassen has received the most citations among the 10 most significant papers on social media addiction, with 405 ( Table 2 ). The main objective of this type of studies was to identify the associations and the roles of different variables as predictors of social media addiction (e.g., ( 19 , 68 , 69 )). According to general addiction models, the excessive and problematic use of digital technologies is described as “being overly concerned about social media, driven by an uncontrollable motivation to log on to or use social media, and devoting so much time and effort to social media that it impairs other important life areas” ( 27 , 70 ). Furthermore, the purpose of several highly cited studies ( 31 , 71 ) was to analyse the connections between young adults' sleep quality and psychological discomfort, depression, self-esteem, and life satisfaction and the severity of internet and problematic social media use, since the health of younger generations and teenagers is of great interest this may help explain the popularity of such papers. Despite being the most recent publication Lin et al.'s work garnered more citations annually. The desire to quantify social media addiction in individuals can also help explain the popularity of studies which try to develop measurement scales ( 42 , 72 ). Some of the highest-ranked publications are devoted to either the presentation of case studies or testing relationships among psychological constructs ( 73 ).

Keyword co-occurrence analysis

The research question, “What are the key thematic areas in social media addiction literature?” was answered using keyword co-occurrence analysis. Keyword co-occurrence analysis is conducted to identify research themes and discover keywords. It mainly examines the relationships between co-occurrence keywords in a wide variety of literature ( 74 ). In this approach, the idea is to explore the frequency of specific keywords being mentioned together.

Utilizing VOSviewer, the authors conducted a keyword co-occurrence analysis to characterize and review the developing trends in the field of social media addiction. The top 10 most frequent keywords are presented in Table 3 . The results indicate that “social media addiction” is the most frequent keyword (178 occurrences), followed by “problematic social media use” (74 occurrences), “internet addiction” (51 occurrences), and “depression” (46 occurrences). As shown in the co-occurrence network ( Figure 5 ), the keywords can be grouped into two major clusters. “Problematic social media use” can be identified as the core theme of the green cluster. In the red cluster, keywords mainly identify a specific aspect of problematic social media use: social media addiction.

www.frontiersin.org

Table 3 . Frequency of occurrence of top 10 keywords.

www.frontiersin.org

Figure 5 . Keywords co-occurrence map. Threshold: 5 co-occurrences.

The results of the keyword co-occurrence analysis for journal articles provide valuable perspectives and tools for understanding concepts discussed in past studies of social media usage ( 75 ). More precisely, it can be noted that there has been a large body of research on social media addiction together with other types of technological addictions, such as compulsive web surfing, internet gaming disorder, video game addiction and compulsive online shopping ( 76 – 78 ). This field of research has mainly been directed toward teenagers, middle school students, and college students and university students in order to understand the relationship between social media addiction and mental health issues such as depression, disruptions in self-perceptions, impairment of social and emotional activity, anxiety, neuroticism, and stress ( 79 – 81 ).

The findings presented in this paper show that there has been an exponential increase in scholarly publications—from two publications in 2013 to 195 publications in 2021. There were 45 publications in 2022 at the time this study was conducted. It was interesting to observe that the US, the UK, and Turkey accounted for 47% of the publications in this field even though none of these countries are in the top 15 countries in terms of active social media penetration ( 82 ) although the US has the third highest number of social media users ( 83 ). Even though China and India have the highest number of social media users ( 83 ), first and second respectively, they rank fifth and tenth in terms of publications on social media addiction or problematic use of social media. In fact, the US has almost double the number of publications in this field compared to China and almost five times compared to India. Even though East Asia, Southeast Asia, and South Asia make up the top three regions in terms of worldwide social media users ( 84 ), except for China and India there have been only a limited number of publications on social media addiction or problematic use. An explanation for that could be that there is still a lack of awareness on the negative consequences of the use of social media and the impact it has on the mental well-being of users. More research in these regions should perhaps be conducted in order to understand the problematic use and addiction of social media so preventive measures can be undertaken.

From the bibliometric analysis, it was found that most of the studies examined used quantitative methods in analyzing data and therefore aimed at testing relationships between variables. In addition, many studies were empirical, aimed at testing relationships based on direct or indirect observations of social media use. Very few studies used theories and for the most part if they did they used the technology acceptance model and social comparison theories. The findings presented in this paper show that none of the studies attempted to create or test new theories in this field, perhaps due to the lack of maturity of the literature. Moreover, neither have very many qualitative studies been conducted in this field. More qualitative research in this field should perhaps be conducted as it could explore the motivations and rationales from which certain users' behavior may arise.

The authors found that almost all the publications on social media addiction or problematic use relied on samples of undergraduate students between the ages of 19–25. The average daily time spent by users worldwide on social media applications was highest for users between the ages of 40–44, at 59.85 min per day, followed by those between the ages of 35–39, at 59.28 min per day, and those between the ages of 45–49, at 59.23 per day ( 85 ). Therefore, more studies should be conducted exploring different age groups, as users between the ages of 19–25 do not represent the entire population of social media users. Conducting studies on different age groups may yield interesting and valuable insights to the field of social media addiction. For example, it would be interesting to measure the impacts of social media use among older users aged 50 years or older who spend almost the same amount of time on social media as other groups of users (56.43 min per day) ( 85 ).

A majority of the studies tested social media addiction or problematic use based on only two social media platforms: Facebook and Instagram. Although Facebook and Instagram are ranked first and fourth in terms of most popular social networks by number of monthly users, it would be interesting to study other platforms such as YouTube, which is ranked second, and WhatsApp, which is ranked third ( 86 ). Furthermore, TikTok would also be an interesting platform to study as it has grown in popularity in recent years, evident from it being the most downloaded application in 2021, with 656 million downloads ( 87 ), and is ranked second in Q1 of 2022 ( 88 ). Moreover, most of the studies focused only on one social media platform. Comparing different social media platforms would yield interesting results because each platform is different in terms of features, algorithms, as well as recommendation engines. The purpose as well as the user behavior for using each platform is also different, therefore why users are addicted to these platforms could provide a meaningful insight into social media addiction and problematic social media use.

Lastly, most studies were cross-sectional, and not longitudinal, aiming at describing results over a certain point in time and not over a long period of time. A longitudinal study could better describe the long-term effects of social media use.

This study was conducted to review the extant literature in the field of social media and analyze the global research productivity during the period ranging from 2013 to 2022. The study presents a bibliometric overview of the leading trends with particular regard to “social media addiction” and “problematic social media use.” The authors applied science mapping to lay out a knowledge base on social media addiction and its problematic use. This represents the first large-scale analysis in this area of study.

A keyword search of “social media addiction” OR “problematic social media use” yielded 553 papers, which were downloaded from Scopus. After performing the Scopus-based investigation of the current literature regarding social media addiction and problematic use, the authors ended up with a knowledge base consisting of 501 documents comprising 455 journal articles, 27 conference papers, 15 articles reviews, 3 books, and 1 conference review.

The geographical distribution trends of scholarly publications on social media addiction or problematic use indicate that the most productive countries were the USA (92), the U.K. (79), and Turkey ( 63 ), which together produced 236 articles. Griffiths (sixty-five articles), Lin (twenty articles), and Pakpour (eighteen articles) were the most productive scholars according to the number of Scopus documents examined in the area of social media addiction and its problematic use. An author co-citation analysis (ACA) was conducted which generated a layout of social media effects on well-being and cyber psychology as well as mental health and digital media addiction in the form of two research literature clusters representing the intellectual structure of social media and its problematic use.

The preferred periodicals in the field of social media addiction and its problematic use were Addictive Behaviors , with 700 citations and 22 publications, followed by Computers in Human Behavior , with 577 citations and 13 publications, and Journal of Behavioral Addictions , with 562 citations and 17 publications. Keyword co-occurrence analysis was used to investigate the key thematic areas in the social media literature, as represented by the top three keyword phrases in terms of their frequency of occurrence, namely, “social media addiction,” “problematic social media use,” and “social media addiction.”

This research has a few limitations. The authors used science mapping to improve the comprehension of the literature base in this review. First and foremost, the authors want to emphasize that science mapping should not be utilized in place of established review procedures, but rather as a supplement. As a result, this review can be considered the initial stage, followed by substantive research syntheses that examine findings from recent research. Another constraint stems from how 'social media addiction' is defined. The authors overcame this limitation by inserting the phrase “social media addiction” OR “problematic social media use” in the search string. The exclusive focus on SCOPUS-indexed papers creates a third constraint. The SCOPUS database has a larger number of papers than does Web of Science although it does not contain all the publications in a given field.

Although the total body of literature on social media addiction is larger than what is covered in this review, the use of co-citation analyses helped to mitigate this limitation. This form of bibliometric study looks at all the publications listed in the reference list of the extracted SCOPUS database documents. As a result, a far larger dataset than the one extracted from SCOPUS initially has been analyzed.

The interpretation of co-citation maps should be mentioned as a last constraint. The reason is that the procedure is not always clear, so scholars must have a thorough comprehension of the knowledge base in order to make sense of the result of the analysis ( 63 ). This issue was addressed by the authors' expertise, but it remains somewhat subjective.

Implications

The findings of this study have implications mainly for government entities and parents. The need for regulation of social media addiction is evident when considering the various risks associated with habitual social media use. Social media addiction may lead to negative consequences for adolescents' school performance, social behavior, and interpersonal relationships. In addition, social media addiction may also lead to other risks such as sexting, social media stalking, cyber-bullying, privacy breaches, and improper use of technology. Given the seriousness of these risks, it is important to have regulations in place to protect adolescents from the harms of social media addiction.

Regulation of social media platforms

One way that regulation could help protect adolescents from the harms of social media addiction is by limiting their access to certain websites or platforms. For example, governments could restrict adolescents' access to certain websites or platforms during specific hours of the day. This would help ensure that they are not spending too much time on social media and are instead focusing on their schoolwork or other important activities.

Another way that regulation could help protect adolescents from the harms of social media addiction is by requiring companies to put warning labels on their websites or apps. These labels would warn adolescents about the potential risks associated with excessive use of social media.

Finally, regulation could also require companies to provide information about how much time each day is recommended for using their website or app. This would help adolescents make informed decisions about how much time they want to spend on social media each day. These proposed regulations would help to protect children from the dangers of social media, while also ensuring that social media companies are more transparent and accountable to their users.

Parental involvement in adolescents' social media use

Parents should be involved in their children's social media use to ensure that they are using these platforms safely and responsibly. Parents can monitor their children's online activity, set time limits for social media use, and talk to their children about the risks associated with social media addiction.

Education on responsible social media use

Adolescents need to be educated about responsible social media use so that they can enjoy the benefits of these platforms while avoiding the risks associated with addiction. Education on responsible social media use could include topics such as cyber-bullying, sexting, and privacy breaches.

Research directions for future studies

A content analysis was conducted to answer the fifth research questions “What are the potential research directions for addressing social media addiction in the future?” The study reveals that there is a lack of screening instruments and diagnostic criteria to assess social media addiction. Validated DSM-V-based instruments could shed light on the factors behind social media use disorder. Diagnostic research may be useful in order to understand social media behavioral addiction and gain deeper insights into the factors responsible for psychological stress and psychiatric disorders. In addition to cross-sectional studies, researchers should also conduct longitudinal studies and experiments to assess changes in users' behavior over time ( 20 ).

Another important area to examine is the role of engagement-based ranking and recommendation algorithms in online habit formation. More research is required to ascertain how algorithms determine which content type generates higher user engagement. A clear understanding of the way social media platforms gather content from users and amplify their preferences would lead to the development of a standardized conceptualization of social media usage patterns ( 89 ). This may provide a clearer picture of the factors that lead to problematic social media use and addiction. It has been noted that “misinformation, toxicity, and violent content are inordinately prevalent” in material reshared by users and promoted by social media algorithms ( 90 ).

Additionally, an understanding of engagement-based ranking models and recommendation algorithms is essential in order to implement appropriate public policy measures. To address the specific behavioral concerns created by social media, legislatures must craft appropriate statutes. Thus, future qualitative research to assess engagement based ranking frameworks is extremely necessary in order to provide a broader perspective on social media use and tackle key regulatory gaps. Particular emphasis must be placed on consumer awareness, algorithm bias, privacy issues, ethical platform design, and extraction and monetization of personal data ( 91 ).

From a geographical perspective, the authors have identified some main gaps in the existing knowledge base that uncover the need for further research in certain regions of the world. Accordingly, the authors suggest encouraging more studies on internet and social media addiction in underrepresented regions with high social media penetration rates such as Southeast Asia and South America. In order to draw more contributions from these countries, journals with high impact factors could also make specific calls. This would contribute to educating social media users about platform usage and implement policy changes that support the development of healthy social media practices.

The authors hope that the findings gathered here will serve to fuel interest in this topic and encourage other scholars to investigate social media addiction in other contexts on newer platforms and among wide ranges of sample populations. In light of the rising numbers of people experiencing mental health problems (e.g., depression, anxiety, food disorders, and substance addiction) in recent years, it is likely that the number of papers related to social media addiction and the range of countries covered will rise even further.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

Author contributions

AP took care of bibliometric analysis and drafting the paper. VB took care of proofreading and adding value to the paper. AS took care of the interpretation of the findings. All authors contributed to the article and approved the submitted version.

Conflict of interest

The authors declare 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

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.

1. Asur S and Huberman BA. “Predicting the future with social media,” in 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Vol. 1 . IEEE (2010). p. 492–9. doi: 10.1109/WI-IAT.2010.63

CrossRef Full Text | Google Scholar

2. Kaye LK. Exploring the “socialness” of social media. Comput. Human Behav. Rep. (2021) 3:2021. doi: 10.1016/j.chbr.2021.100083

3. Boyd DM and Ellison NB. Social network sites: definition, history, and scholarship. J Computer-Med Commun. (2007) 13:210–30. doi: 10.1111/j.1083-6101.2007.00393.x

4. Marengo D, Fabris MA, Longobardi C, Settanni M. Smartphone and social media use contributed to individual tendencies towards social media addiction in Italian adolescents during the COVID-19 pandemic. Addictive Behav. (2022) 126:107204. doi: 10.1016/j.addbeh.2021.107204

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Alshamrani S, Abusnaina A, Abuhamad M, Nyang D, Mohaisen D. “Hate, obscenity, and insults: Measuring the exposure of children to inappropriate comments in youtube,” in Companion Proceedings of the Web Conference . (2021). p. 508–515. doi: 10.1145/3442442.3452314

6. Malesev S, Cherry M. Digital and social media marketing-growing market share for construction SMEs. Construction Econom. Build. (2021) 21:65–82. doi: 10.5130/AJCEB.v21i1.7521

7. We Are Social. Digital 2022: Global Overview Report. (2022). Available online at: https://datareportal.com/reports/digital-2022-global-overview-report (accessed July 18, 2022).

Google Scholar

8. Latikka R, Koivula A, Oksa R, Savela N, Oksanen A. Loneliness and psychological distress before and during the COVID-19 pandemic: relationships with social media identity bubbles. Soc Sci Med. (2022) 293:114674. doi: 10.1016/j.socscimed.2021.114674

9. American Academy of Pediatrics. Supporting the health care transition from adolescence to adulthood in the medical home. Pediatrics. (2011) 128:182–200. doi: 10.1542/peds.2018-2587

10. Royal Society for Public Health. Social media addiction: The role of content. J Behav Addict. (2017) 9:64–77. doi: 10.1556/2006.6.2017.058

11. Blease CR. Too many “friends”, too few “likes”? Evolutionary psychology and Facebook depression. Rev. General Psychol. (2015) 19:1–13. doi: 10.1037/gpr0000030

12. Hasinoff AA. Sexting as media production: rethinking social media and sexuality. New Media Soc. (2013) 15:449–65. doi: 10.1177/1461444812459171

13. Sapone L. Moving fast and breaking things: an analysis of social media's revolutionary effects on culture and its impending regulation. Duq L Rev. (2021) 59:362.

14. Whittaker E, Kowalski RM. Cyberbullying via social media. J School Violence. (2015) 14:11–29. doi: 10.1080/15388220.2014.949377

15. Saravanakumar K, Deepa K. On privacy and security in social media–a comprehensive study. Procedia Computer Sci. (2016) 78:114–9. doi: 10.1016/j.procs.2016.02.019

16. Kalika M. Social networking continuance: When habit leads to information overload. Inf Process Manag. (2015) 5:10–31. doi: 10.18151/7217377

17. Kelly Y, Zilanawala A, Booker C, Sacker A. Social media use and adolescent mental health: findings from the UK Millennium cohort study. EClinicalMedicine. (2018) 6:59–68. doi: 10.1016/j.eclinm.2018.12.005

18. Twenge JM, Campbell WK. Associations between screen time and lower psychological well-being among children and adolescents: evidence from a population-based study. Prevent Med Rep. (2018) 12:271–83. doi: 10.1016/j.pmedr.2018.10.003

19. Andreassen CS, Pallesen S, Griffiths MD. The relationship between addictive use of social media, narcissism, and self-esteem: Findings from a large national survey. Addict Behav. (2014) 64:287–93. doi: 10.1016/j.addbeh.2016.03.006

20. Khan NF, Khan MN. A bibliometric analysis of peer-reviewed literature on smartphone addiction and future research agenda. Asia-Pacific J Bus Admin. (2021) 14:199–201. doi: 10.1108/APJBA-09-2021-0430

21. Baran KS, Ghaffari H. The manifold research fields of Facebook: a bibliometric analysis. J Information Sci Theory Pract. (2017) 5:33–47. doi: 10.1633/JISTaP.2017.5.2.3

22. Rejeb A, Rejeb K, Abdollahi A, Treiblmaier H. The big picture on Instagram research: Insights from a bibliometric analysis. Telematics Informat. (2022) 25:101876. doi: 10.1016/j.tele.2022.101876

23. D'Arienzo MC, Boursier V, Griffiths MD. Addiction to social media and attachment styles: A systematic literature review. Int J Ment Health Addict. (2019) 17:1094–118. doi: 10.1007/s11469-019-00082-5

24. Kırcaburun K, Griffiths MD. Problematic Instagram use: The role of perceived feeling of presence and escapism. Int J Ment Health Addict. (2019) 17:909–21. doi: 10.1007/s11469-018-9895-7

25. Ho SS, Lwin MO, Lee EW. Till logout do us part? Comparison of factors predicting excessive social network sites use and addiction between Singaporean adolescents and adults. Comput. Human Behav. (2017) 75:632–42. doi: 10.1016/j.chb.2017.06.002

26. Kuss DJ, Griffiths MD. Online social networking and addiction—a review of the psychological literature. Int J Environ Res Public Health. (2011) 8:3528–52. doi: 10.3390/ijerph8093528

27. Buote VM, Wood E, Pratt M. Exploring similarities and differences between online and offline friendships: The role of attachment style. Comput Human Behav. (2009) 25:560–7. doi: 10.1016/j.chb.2008.12.022

28. Andreassen CS, Billieux J, Griffiths MD, Kuss DJ, Demetrovics Z, Mazzoni E, et al. The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychol Addict Behav. (2016) 30:252. doi: 10.1037/adb0000160

29. Seidman G. Self-presentation and belonging on Facebook: How personality influences social media use and motivations. Pers Individ Dif. (2013) 54:402–7. doi: 10.1016/j.paid.2012.10.009

30. Saucier G. Mini-Markers: A brief version of Goldberg's unipolar Big-Five markers. J Personal Assess. (1994) 63:506–16. doi: 10.1207/s15327752jpa6303_8

31. Blackwell D, Leaman C, Tramposch R, Osborne C, Liss M. Extraversion, neuroticism, attachment style and fear of missing out as predictors of social media use and addiction. Personal Individ Diff. (2017) 116:69–72. doi: 10.1016/j.paid.2017.04.039

32. Hawk ST, van den Eijnden RJ, van Lissa CJ, ter Bogt TF. Narcissistic adolescents' attention-seeking following social rejection: Links with social media disclosure, problematic social media use, and smartphone stress. Comput Human Behav. (2019) 92:65–75. doi: 10.1016/j.chb.2018.10.032

33. Longstreet P, Brooks S. Life satisfaction: a key to managing internet and social media addiction. Technol Soc. (2017) 50:73–7. doi: 10.1016/j.techsoc.2017.05.003

34. Marriott TC, Buchanan T. The true self online: personality correlates of preference for self-expression online, and observer ratings of personality online and offline. Comput Human Behav. (2014) 32:171–7. doi: 10.1016/j.chb.2013.11.014

35. Orr ES, Sisic M, Ross C, Simmering MG, Arseneault JM, Orr RR. The influence of shyness on the use of Facebook in an undergraduate sample. Cyberpsychol Behav. (2009) 12:337–40. doi: 10.1089/cpb.2008.0214

36. Brunet PM, Schmidt LA. Is shyness context specific? Relation between shyness and online self-disclosure with and without a live webcam in young adults. J Res Pers. (2007) 41:938–45.

37. Roberts SG, Wilson R, Fedurek P, Dunbar RI. Individual differences and personal social network size and structure. Pers Individ Dif. (2000) 44:954–64. doi: 10.1016/j.jrp.2006.09.001

38. Wegmann E, Müller SM, Turel O, Brand M. Interactions of impulsivity, general executive functions, and specific inhibitory control explain symptoms of social-networks-use disorder: an experimental study. Sci Rep. (2020) 10:3866. doi: 10.1038/s41598-020-60819-4

39. Motoki K, Suzuki S, Kawashima R, Sugiura M. A combination of self-reported data and social-related neural measures forecasts viral marketing success on social media. J Interactive Market. (2020) 52:99–117. doi: 10.1016/j.intmar.2020.06.003

40. Zendle D, Bowden-Jones H. Is excessive use of social media an addiction? BMJ . (2019) 365:2171. doi: 10.1136/bmj.l2171

41. Bányai F, Zsila Á, Király O, Maraz A, Elekes Z, Griffiths MD, et al. Problematic social media use: results from a large-scale nationally representative adolescent sample. PLoS ONE. (2017) 12:e0169839. doi: 10.1371/journal.pone.0169839

42. Van den Eijnden RJ, Lemmens JS, Valkenburg PM. The social media disorder scale. Comput Human Behav. (2016) 61:478–87. doi: 10.1016/j.chb.2016.03.038

43. Franchina V, Vanden Abeele M, Van Rooij AJ, Lo Coco G, De Marez L. Fear of missing out as a predictor of problematic social media use and phubbing behavior among Flemish adolescents. Int J Environ Res Public Health. (2018) 15:2319. doi: 10.3390/ijerph15102319

44. Scott H, Woods HC. Fear of missing out and sleep: Cognitive behavioural factors in adolescents' nighttime social media use. J Adolescence. (2018) 68:61–5. doi: 10.1016/j.adolescence.2018.07.009

45. Boer M, Van Den Eijnden RJ, Boniel-Nissim M, Wong SL, Inchley JC, Badura P, et al. Adolescents' intense and problematic social media use and their well-being in 29 countries. J Adolescent Health. (2020) 66:S89–99. doi: 10.1016/j.jadohealth.2020.02.014

46. Paakkari L, Tynjälä J, Lahti H, Ojala K, Lyyra N. Problematic social media use and health among adolescents. Int J Environ Res Public Health. (2021) 18:1885. doi: 10.3390/ijerph18041885

47. Lee EW, Ho SS, Lwin MO. Explicating problematic social network sites use: a review of concepts, theoretical frameworks, and future directions for communication theorizing. N Media Soc. (2017) 19:308–26. doi: 10.1177/1461444816671891

48. Inchley JC, Stevens GW, Samdal O, Currie DB. Enhancing understanding of adolescent health and well-being: the health behaviour in school-aged children study. J Adolescent Health. (2020) 66:S3–5. doi: 10.1016/j.jadohealth.2020.03.014

49. Buda G, Lukoševičiut, e J, Šalčiunaite L, Šmigelskas K. Possible effects of social media use on adolescent health behaviors and perceptions. Psychol Rep. (2021) 124:1031–48. doi: 10.1177/0033294120922481

50. Vannucci A, McCauley Ohannessian C. Social media use subgroups differentially predict psychosocial well-being during early adolescence. J Youth Adolescence. (2019) 48:1469–93. doi: 10.1007/s10964-019-01060-9

51. Lewycka S, Clark T, Peiris-John R, Fenaughty J, Bullen P, Denny S, et al. Downwards trends in adolescent risk-taking behaviours in New Zealand: Exploring driving forces for change. J Paediatr Child Health. (2018) 54:602–8. doi: 10.1111/jpc.13930

52. Vannucci A, Simpson EG, Gagnon S, Ohannessian CM. Social media use and risky behaviors in adolescents: a meta-analysis. J Adolescence. (2020) 79:258–74. doi: 10.1016/j.adolescence.2020.01.014

53. Uncapher MR, Lin L, Rosen LD, Kirkorian HL, Baron NS, Bailey K, et al. Media multitasking and cognitive, psychological, neural, learning differences. Pediatrics . (2017) 140(Suppl 2):S62–6. doi: 10.1542/peds.2016-1758D

54. Ali I, Balta M, Papadopoulos T. Social media platforms and social enterprise: bibliometric analysis and systematic review. Int J Inform Manage. (2022) 10:510. doi: 10.1016/j.ijinfomgt.2022.102510

55. Aparicio-Martinez P, Perea-Moreno AJ, Martinez-Jimenez MP, Redel-Macías MD, Vaquero-Abellan M, Pagliari C. A bibliometric analysis of the health field regarding social networks and young people. Int J Environ Res Public Health. (2019) 16:4024. doi: 10.3390/ijerph16204024

56. Sharif SP, Yeoh KK. Excessive Social Networking Sites Use and Online Compulsive Buying in Young Adults: the Mediating Role of Money Attitude. Young Consumers (2018).

57. Moreno-Guerrero AJ, Gómez-García G, López-Belmonte J, Rodríguez-Jiménez C. Internet addiction in the web of science database: a review of the literature with scientific mapping. Int J Environ Res Public Health. (2020) 17:27–53. doi: 10.3390/ijerph17082753

58. Merig, ó JM, Mas-Tur A, Roig-Tierno N, Ribeiro-Soriano D. A bibliometric overview of the Journal of Business Research between 1973 and 2014. J Business Res. (2015) 68:2645–53. doi: 10.1016/j.jbusres.2015.04.006

59. Kumar S, Pandey N, Burton B, Sureka R. Research patterns and intellectual structure of managerial auditing journal: a retrospective using bibliometric analysis during 1986–2019. Managerial Auditing J . (2021) 36:280–313. doi: 10.1108/MAJ-12-2019-2517

60. Van Eck NJ, Waltman L. CitNetExplorer: a new software tool for analyzing and visualizing citation networks. J Informetr. (2014) 8:802–23. doi: 10.1016/j.joi.2014.07.006

61. Durieux V, Gevenois PA. Bibliometric indicators: quality measurements of scientific publication. Radiology. (2010) 255:342–51. doi: 10.1148/radiol.09090626

62. Small H. Visualizing science by citation mapping. J Am Soc Information Sci. (1999) 50:799–813. doi: 10.1002/(SICI)1097-4571(1999)50:9 < 799::AID-ASI9>3.0.CO;2-G

63. Zupic I, Cater T. Bibliometric methods in management and organization. Organiz Res Methods. (2015) 18:429–72. doi: 10.1177/1094428114562629

64. Karjalainen J, Valtakoski A, KauraNen I. (2019). Interfirm network structure and firm resources: Towards a unifying concept. In: A Network Approach in Strategic Management: Emerging Trends and Research Concepts , Vol. 17. Fundacja Cognitione (2019). p. 227. doi: 10.7341/20211737

65. Pattnaik D. A bibliometric retrospection of marketing from the lens of psychology: Insights from psychology & marketing. Psychol Market. (2020) 38:834–65. doi: 10.1002/mar.21472

66. Chu SC, Chen HT, Gan C. Consumers' engagement with corporate social responsibility (CSR) communication in social media: evidence from China and the United States. J Business Res. (2020) 110:260–71. doi: 10.1016/j.jbusres.2020.01.036

67. Bush VD, Gilbert FW. The web as a medium: an exploratory comparison of internet users versus newspaper readers. J Market Theory Pract. (2002) 10:1–10. doi: 10.1080/10696679.2002.11501905

68. Griffiths MD, Kuss DJ, Demetrovics Z. Social networking addiction: An overview of preliminary findings. Behav Addict. (2014) 2014:119–41. doi: 10.1016/B978-0-12-407724-9.00006-9

69. Tang J, Chang Y, Aggarwal C, Liu H. A survey of signed network mining in social media. ACM Comput Surveys (CSUR). (2016) 49:1–37. doi: 10.1145/2956185

70. Griffiths MD. En “komponenter” modell for avhengighet innenfor en biopsykososial ramme. J Subst-bruk. (2005) 10:191–7.

71. Lin CY, Broström A, Griffiths MD, Pakpour AH. Investigating mediated effects of fear of COVID-19 and COVID-19 misunderstanding in the association between problematic social media use, psychological distress, and insomnia. Internet Intervent. (2020) 21:100345. doi: 10.1016/j.invent.2020.100345

72. Monacis L, De Palo V, Griffiths MD, Sinatra M. Social networking addiction, attachment style, and validation of the Italian version of the Bergen Social Media Addiction Scale. J Behav Addict. (2017) 6:178–86. doi: 10.1556/2006.6.2017.023

73. Gupta K. What is social media? How is it affecting adolescent's mental health? Eur Biomedical J. (2015) 410:51–65. doi: 10.1000/182

74. Zhao Z, Zhu H, Xue Z, Liu Z, Tian J, Chua MCH, et al. An image-text consistency driven multimodal sentiment analysis approach for social media. Inf Process Manag. (2019) 56:102097. doi: 10.1016/j.ipm.2019.102097

75. Small H. Co-citation in the scientific literature: a new measure of the relationship between two documents. J Am Soc Informat Sci. (1973) 24:265–9. doi: 10.1002/asi.4630240406

76. Su W, Han X, Yu H, Wu Y, Potenza MN. Do men become addicted to internet gaming and women to social media? A meta-analysis examining gender-related differences in specific internet addiction. Comput Human Behav. (2020) 113:106480. doi: 10.1016/j.chb.2020.106480

77. Albort-Morant G, Henseler J, Leal-Millán A, Cepeda-Carrión G. Mapping the field: a bibliometric analysis of green innovation. Sustainability . (2017) 9:1011. doi: 10.3390/su9061011

78. Wilson K, Fornasier S, White KM. Psychological predictors of young adults' use of social networking sites. Cyberpsychol Behav Social Netw . (2010) 13:173–7. doi: 10.1089/cyber.2009.0094

79. Barbera LD, Paglia LF, Valsavoia R. Social network and addiction. Stud Health Technol Inform. (2009) 144:33–6. doi: 10.3390/ijerph14030311

80. Hussain Z, Griffiths MD. Problematic social networking site use and comorbid psychiatric disorders: a systematic review of recent large-scale studies. Front Psychiatr . (2018) 5:686. doi: 10.3389/fpsyt.2018.00686

81. American Academy of Pediatric Dentistry. Guideline on Psychological Therapy . Chicago: American Academy of Pediatric (2008). Available online at: http://www.aapd.org/media/Policies_Guidelines/PsychologicalTherapy.pdf (accessed July 18, 2022).

82. Statista. Number of Smartphone Subscriptions Worldwide from 2016 to 2026. (2022). Available online at: https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/ (accessed July 10, 2022).

83. Statista.com. Share of Global Mobile Website Traffic 2015–2021. (2022). Available online at: https://www.statista.com/statistics/277125/share-of-website-traffic-coming-from-mobile-devices/ (accessed July 18, 2022).

84. Statista.com. Global Social Media Traffic 2015–2021. (2022). Available online at: https://www.statista.com/statistics/277115/social-media-traffic/ (accessed July 18, 2022).

85. Statista.com. Asia Social Media Traffic 2015–2021. (2022). Available online at: https://www.statista.com/statistics/27751/asia-socia-media-traffic/ (accessed July 18, 2022).

86. Statista.com. Asia Website Traffic 2015-2021. (2022). Available online at: https://www.statista.com/statistics/277557/global-asia-website-traffic/ (accessed July 18, 2022).

87. de Valle MK, Gallego-García M, Williamson P, Wade TD. Social media, body image, and the question of causation: Meta-analyses of experimental and longitudinal evidence. Body Image. (2021) 39:276–92. doi: 10.1016/j.bodyim.2021.10.001

88. Koetsier M. Current issues of social media and crisis communication. Soc Media Crises Commun. (2022) 20–32.

89. Paschke K, Austermann MI, Thomasius R. ICD-11-based assessment of social media use disorder in adolescents: development and validation of the social media use disorder scale for adolescents. Front Psychiatr. (2021) 12:661483. doi: 10.3389/fpsyt.2021.661483

90. Horwitz J. The Facebook Whistleblower, Frances Haugen, Says She Wants to Fix the Company, Not Harm It . New York, NY: The Wall Street Journal (2021). p. 3.

91. Raschke RL, Krishen AS, Kachroo P. Understanding the components of information privacy threats for location-based services. J Informat Syst. (2014) 28:227–42. doi: 10.2308/isys-50696

Keywords: bibliometric analysis, social media, social media addiction, problematic social media use, research trends

Citation: Pellegrino A, Stasi A and Bhatiasevi V (2022) Research trends in social media addiction and problematic social media use: A bibliometric analysis. Front. Psychiatry 13:1017506. doi: 10.3389/fpsyt.2022.1017506

Received: 12 August 2022; Accepted: 24 October 2022; Published: 10 November 2022.

Reviewed by:

Copyright © 2022 Pellegrino, Stasi and Bhatiasevi. 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: Alfonso Pellegrino, alfonso.pellegrino@sasin.edu ; Veera Bhatiasevi, veera.bhatiasevi@mahidol.ac.th

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.

U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings
  • Advanced Search
  • Journal List
  • Front Psychol

Social Media Use and Mental Health and Well-Being Among Adolescents – A Scoping Review

Viktor schønning.

1 Department of Health Promotion, Norwegian Institute of Public Health, Bergen, Norway

Gunnhild Johnsen Hjetland

Leif edvard aarø, jens christoffer skogen.

2 Alcohol and Drug Research Western Norway, Stavanger University Hospital, Stavanger, Norway

3 Faculty of Health Sciences, University of Stavanger, Stavanger, Norway

Associated Data

Introduction: Social media has become an integrated part of daily life, with an estimated 3 billion social media users worldwide. Adolescents and young adults are the most active users of social media. Research on social media has grown rapidly, with the potential association of social media use and mental health and well-being becoming a polarized and much-studied subject. The current body of knowledge on this theme is complex and difficult-to-follow. The current paper presents a scoping review of the published literature in the research field of social media use and its association with mental health and well-being among adolescents.

Methods and Analysis: First, relevant databases were searched for eligible studies with a vast range of relevant search terms for social media use and mental health and well-being over the past five years. Identified studies were screened thoroughly and included or excluded based on prior established criteria. Data from the included studies were extracted and summarized according to the previously published study protocol.

Results: Among the 79 studies that met our inclusion criteria, the vast majority (94%) were quantitative, with a cross-sectional design (57%) being the most common study design. Several studies focused on different aspects of mental health, with depression (29%) being the most studied aspect. Almost half of the included studies focused on use of non-specified social network sites (43%). Of specified social media, Facebook (39%) was the most studied social network site. The most used approach to measuring social media use was frequency and duration (56%). Participants of both genders were included in most studies (92%) but seldom examined as an explanatory variable. 77% of the included studies had social media use as the independent variable.

Conclusion: The findings from the current scoping review revealed that about 3/4 of the included studies focused on social media and some aspect of pathology. Focus on the potential association between social media use and positive outcomes seems to be rarer in the current literature. Amongst the included studies, few separated between different forms of (inter)actions on social media, which are likely to be differentially associated with mental health and well-being outcomes.

In just a few decades, the use of social media have permeated most areas of our society. For adolescents, social media play a particularly large part in their lives as indicated by their extensive use of several different social media platforms ( Ofcom, 2018 ). Furthermore, the use of social media and types of platforms offered have increased at such a speed that there is reason to believe that scientific knowledge about social media in relation to adolescents’ health and well-being is scattered and incomplete ( Orben, 2020 ). Nevertheless, research findings indicating the potential negative effects of social media on mental health and well-being are frequently reported in traditional media (newspapers, radio, TV) ( Bell et al., 2015 ). Within the scientific community, however, there are ongoing debates regarding the impact and relevance of social media in relation to mental health and well-being. For instance, Twenge and Campbell (2019) stated that use of digital technology and social media have a negative impact on well-being, while Orben and Przybylski (2019) argued that the association between digital technology use and adolescent well-being is so small that it is more or less inconsequential. Research on social media use is a new focus area, and it is therefore important to get an overview of the studies performed to date, and describe the subject matter studies have investigated in relation to the effect of social media use on adolescents mental health and well-being. Also, research gaps in this emerging research field is important to highlight as it may guide future research in new and meritorious directions. A scoping review is therefore deemed necessary to provide a foundation for further research, which in time will provide a knowledge base for policymaking and service delivery.

This scoping review will help provide an overall understanding of the main foci of research within the field of social media and mental health and well-being among adolescents, as well as the type of data sources and research instruments used so far. Furthermore, we aim to highlight potential gaps in the research literature ( Arksey and O’Malley, 2005 ). Even though a large number of studies on social media use and mental health with different vantage points has been conducted over the last decade, we are not aware of any broad-sweeping scoping review covering this area.

This scoping review aims to give an overview of the main research questions that have been focused on with regard to use of social media among adolescents in relation to mental health and well-being. Both quantitative and qualitative studies are of interest. Three specific secondary research questions will be addressed and together with the main research question serve as a template for organizing the results:

  • • Which aspects of mental health and well-being have been the focus or foci of research so far?
  • • Has the research focused on different research aims across gender, ethnicity, socio-economic status, geographic location? What kind of findings are reported across these groups?
  • • Organize and describe the main sources of evidence related to social media that have been used in the studies identified.

Defining Adolescence and Social Media

In the present review, adolescents are defined as those between 13 and 19 years of age. We chose the mean age of 13 as our lower limit as nearly all social media services require users to be at least 13 years of age to access and use their services ( Childnet International, 2018 ). All pertinent studies which present results relevant for this age range is within the scope of this review. For social media we used the following definition by Kietzmann et al. (2011 , p. 1): “Social media employ mobile and web-based technologies to create highly interactive platforms via which individuals and communities share, co-create, discuss, and modify user-generated content.” We also employed the typology described by Kaplan and Haenlein’s classification scheme across two axes: level of self-presentation and social presence/media richness ( Kaplan and Haenlein, 2010 ). The current scoping review adheres to guidelines and recommendations stated by Tricco et al. (2018) .

See protocol for further details about the definitions used ( Schønning et al., 2020 ).

Data Sources and Search Strategy

A literature search was performed in OVID Medline, OVID Embase, OVID PsycINFO, Sociological Abstracts (proquest), Social Services Abstracts (proquest), ERIC (proquest), and CINAHL. The search strategy combined search terms for adolescents, social media and mental health or wellbeing. The database-controlled vocabulary was used for searching subject headings, and a large spectrum of synonyms with appropriate truncations was used for searching title, abstract, and author keywords. A filter for observational studies was applied to limit the results. The search was also limited to publications from 2014 to current. The search strategy was translated between each database. An example of full strategy for Embase is attached as Supplementary Material .

Study Selection: Exclusion and Inclusion Criteria

The exclusion and inclusion criteria are detailed in the protocol ( Schønning et al., 2020 ). Briefly, we included English language peer-reviewed quantitative- or qualitative papers or systematic reviews published within the last 5 years with an explicit focus on mental health/well-being and social media. Non-empirical studies, intervention studies, clinical studies and publications not peer-reviewed were excluded. Intervention studies and clinical studies were excluded as we sought to not introduce too much heterogeneity in design and our focus was on observational studies. The criteria used for study selection was part of an iterative process which was described in detail in the protocol ( Schønning et al., 2020 ). As per the study protocol ( Schønning et al., 2020 ), and in line with scoping review guidelines ( Peters et al., 2015 , 2017 ; Tricco et al., 2018 ), we did not assess methodological quality or risk of bias of the included studies.

The selection process is illustrated by a flow-chart indicating the stages from unsorted search results to the number of included studies (see Figure 1 ). Study selection was accomplished and organized using the Rayyan QCRI software 1 . The inclusion and exclusion process was performed independently by VS and JCS. The interrater agreement was κ = 0.87, indicating satisfactory agreement.

An external file that holds a picture, illustration, etc.
Object name is fpsyg-11-01949-g001.jpg

Flowchart of exclusion process from unsorted results to included studies.

Data Extraction and Organization

Details of the data extracted is described in the protocol. Three types of information were extracted, bibliographic information, information about study design and subject matter information. Subject matter information included aim of study, how social media and mental health/well-being was measured, and main findings of the study.

Visualization of Words From the Titles of the Included Studies

The most frequently occurring words and bigrams in the titles of the included studies are presented in Figures 2 , ​ ,3. 3 . The following procedure was used to generate Figure 1 : First, a text file containing all titles were imported into R as a data frame ( R Core Team, 2014 ). The data frame was processed using the “tidy text”-package with required additional packages ( Silge and Robinson, 2016 ). Second, numbers and commonly used words with little inherent meaning (so called “stop words,” such as “and,” “of,” and “in”), were removed from the data frame using the three available lexicons in the “tidy-text”-package ( Silge and Robinson, 2016 ). Furthermore, variations of “adolescents” (e.g., “adolescent,” “adolescence,” and “adolescents”) and “social media” (e.g., “social media,” “social networking,” “online social networks”) were removed from the data frame. Third, the resulting data frame was sorted based on frequency of unique words, and words occurring only once were removed. The final data frame is presented as a word cloud in Figure 1 ( N = 113). The same procedure as described above was employed to generate commonly occurring bigrams (two words occurring adjacent to each other), but without removing bigrams occurring only once ( N = 231). The word clouds were generated using the “wordcloud2”-package in R ( Lang and Chien, 2018 ). For Figure 1 , shades of blue indicate word frequencies >2 and green a frequency of 2. For Figure 2 , shades of blue indicate bigram frequencies of >1 and green a frequency of 1.

An external file that holds a picture, illustration, etc.
Object name is fpsyg-11-01949-g002.jpg

Word cloud from the titles of the included studies. Most frequent words, excluding variations of “adolescence” and “social media.” N = 113. Shades of blue indicate word frequencies >2 and green a frequency of 2. The size of each word is indicative of its relative frequency of occurrence.

An external file that holds a picture, illustration, etc.
Object name is fpsyg-11-01949-g003.jpg

Word cloud from the titles of the included studies. Bigrams from the titles of the included studies, excluding variations of “adolescence” and “social media.” N = 231. Shades of blue indicate bigram frequencies of >1 and green a frequency of 1. The size of each bigram is indicative of its relative frequency of occurrence.

Characteristics of the Included Studies

Of 7927 unique studies, 79 (1%) met our inclusion criteria ( Aboujaoude et al., 2015 ; Banjanin et al., 2015 ; Banyai et al., 2017 ; Barry et al., 2017 ; Best et al., 2014 , 2015 ; Booker et al., 2018 ; Bourgeois et al., 2014 ; Boyle et al., 2016 ; Brunborg et al., 2017 ; Burnette et al., 2017 ; Colder Carras et al., 2017 ; Critchlow et al., 2019 ; Cross et al., 2015 ; Curtis et al., 2018 ; de Lenne et al., 2018 ; de Vries et al., 2016 ; Erfani and Abedin, 2018 ; Erreygers et al., 2018 ; Fahy et al., 2016 ; Ferguson et al., 2014 ; Fisher et al., 2016 ; Foerster and Roosli, 2017 ; Foody et al., 2017 ; Fredrick and Demaray, 2018 ; Frison and Eggermont, 2016 , 2017 ; Geusens and Beullens, 2017 , 2018 ; Hamm et al., 2015 ; Hanprathet et al., 2015 ; Harbard et al., 2016 ; Hase et al., 2015 ; Holfeld and Mishna, 2019 ; Houghton et al., 2018 ; Jafarpour et al., 2017 ; John et al., 2018 ; Kim et al., 2019 ; Kim, 2017 ; Koo et al., 2015 ; Lai et al., 2018 ; Larm et al., 2017 , 2019 ; Marchant et al., 2017 ; Marengo et al., 2018 ; Marques et al., 2018 ; Meier and Gray, 2014 ; Memon et al., 2018 ; Merelle et al., 2017 ; Neira and Barber, 2014 ; Nesi et al., 2017a , b ; Niu et al., 2018 ; Nursalam et al., 2018 ; Oberst et al., 2017 ; O’Connor et al., 2014 ; O’Reilly et al., 2018 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Richards et al., 2015 ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Sampasa-Kanyinga and Chaput, 2016 ; Sampasa-Kanyinga and Lewis, 2015 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; Spears et al., 2015 ; Throuvala et al., 2019 ; Tiggemann and Slater, 2017 ; Tseng and Yang, 2015 ; Twenge and Campbell, 2019 ; Twenge et al., 2018 ; van den Eijnden et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ; Wolke et al., 2017 ; Woods and Scott, 2016 ; Yan et al., 2017 ). Among the included studies, 74 (94%) are quantitative ( Aboujaoude et al., 2015 ; Banjanin et al., 2015 ; Banyai et al., 2017 ; Barry et al., 2017 ; Best et al., 2014 ; Booker et al., 2018 ; Bourgeois et al., 2014 ; Boyle et al., 2016 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Critchlow et al., 2019 ; Cross et al., 2015 ; Curtis et al., 2018 ; de Lenne et al., 2018 ; de Vries et al., 2016 ; Erfani and Abedin, 2018 ; Erreygers et al., 2018 ; Fahy et al., 2016 ; Ferguson et al., 2014 ; Fisher et al., 2016 ; Foerster and Roosli, 2017 ; Foody et al., 2017 ; Fredrick and Demaray, 2018 ; Frison and Eggermont, 2016 , 2017 ; Geusens and Beullens, 2017 , 2018 ; Hamm et al., 2015 ; Hanprathet et al., 2015 ; Harbard et al., 2016 ; Hase et al., 2015 ; Houghton et al., 2018 ; Jafarpour et al., 2017 ; John et al., 2018 ; Kim et al., 2019 ; Kim, 2017 ; Koo et al., 2015 ; Lai et al., 2018 ; Larm et al., 2017 , 2019 ; Marchant et al., 2017 ; Marengo et al., 2018 ; Marques et al., 2018 ; Meier and Gray, 2014 ; Memon et al., 2018 ; Merelle et al., 2017 ; Neira and Barber, 2014 ; Nesi et al., 2017a , b ; Niu et al., 2018 ; Nursalam et al., 2018 ; Oberst et al., 2017 ; O’Connor et al., 2014 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Richards et al., 2015 ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Sampasa-Kanyinga and Chaput, 2016 ; Sampasa-Kanyinga and Lewis, 2015 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; Spears et al., 2015 ; Tiggemann and Slater, 2017 ; Tseng and Yang, 2015 ; Twenge and Campbell, 2019 ; Twenge et al., 2018 ; van den Eijnden et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ; Wolke et al., 2017 ; Woods and Scott, 2016 ; Yan et al., 2017 ), three are qualitative ( O’Reilly et al., 2018 ; Burnette et al., 2017 ; Throuvala et al., 2019 ), and two use mixed methods ( Best et al., 2015 ; Holfeld and Mishna, 2019 ) (see Supplementary Tables 1 , 2 in the Supplementary Material for additional details extracted from all included studies). In relation to study design, 45 (57%) used a cross-sectional design ( Bourgeois et al., 2014 ; Ferguson et al., 2014 ; Meier and Gray, 2014 ; Neira and Barber, 2014 ; O’Connor et al., 2014 ; Banjanin et al., 2015 ; Hanprathet et al., 2015 ; Hase et al., 2015 ; Koo et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Spears et al., 2015 ; Tseng and Yang, 2015 ; Frison and Eggermont, 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Woods and Scott, 2016 ; Banyai et al., 2017 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Larm et al., 2017 , 2019 ; Merelle et al., 2017 ; Oberst et al., 2017 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Tiggemann and Slater, 2017 ; Wolke et al., 2017 ; Yan et al., 2017 ; de Lenne et al., 2018 ; Erreygers et al., 2018 ; Fredrick and Demaray, 2018 ; Geusens and Beullens, 2018 ; Lai et al., 2018 ; Marengo et al., 2018 ; Marques et al., 2018 ; Niu et al., 2018 ; Nursalam et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ; Critchlow et al., 2019 ; Kim et al., 2019 ; Twenge and Campbell, 2019 ), 17 used a longitudinal design ( Cross et al., 2015 ; Boyle et al., 2016 ; de Vries et al., 2016 ; Fahy et al., 2016 ; Frison and Eggermont, 2016 ; Harbard et al., 2016 ; Foerster and Roosli, 2017 ; Geusens and Beullens, 2017 ; Kim, 2017 ; Nesi et al., 2017a , b ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Booker et al., 2018 ; Houghton et al., 2018 ; van den Eijnden et al., 2018 ; Holfeld and Mishna, 2019 ), seven were systematic reviews ( Aboujaoude et al., 2015 ; Best et al., 2015 ; Fisher et al., 2016 ; Marchant et al., 2017 ; Erfani and Abedin, 2018 ; John et al., 2018 ; Memon et al., 2018 ), two were meta-analyses ( Foody et al., 2017 : Curtis et al., 2018 ), one was a causal-comparative study ( Jafarpour et al., 2017 ), one was a review article ( Richards et al., 2015 ), one used a time-lag design ( Twenge et al., 2018 ), one was a scoping review ( Hamm et al., 2015 ), three used a focus-group interview design ( Burnette et al., 2017 ; O’Reilly et al., 2018 ; Throuvala et al., 2019 ), and one study used a combined survey and focus-group design ( Best et al., 2014 ).

The most common study settings were schools [ N = 42 (54%)] ( Best et al., 2014 ; Bourgeois et al., 2014 ; Meier and Gray, 2014 ; Neira and Barber, 2014 ; O’Connor et al., 2014 ; Banjanin et al., 2015 ; Hanprathet et al., 2015 ; Hase et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Frison and Eggermont, 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Woods and Scott, 2016 ; Banyai et al., 2017 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Foerster and Roosli, 2017 ; Geusens and Beullens, 2017 , 2018 ; Kim, 2017 ; Larm et al., 2017 , 2019 ; Merelle et al., 2017 ; Nesi et al., 2017a , b ; Przybylski and Bowes, 2017 ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Tiggemann and Slater, 2017 ; de Lenne et al., 2018 ; Fredrick and Demaray, 2018 ; Houghton et al., 2018 ; Lai et al., 2018 ; Marengo et al., 2018 ; Niu et al., 2018 ; Nursalam et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; van den Eijnden et al., 2018 ; Wang et al., 2018 ; Holfeld and Mishna, 2019 ; Kim et al., 2019 ). Fourteen of the included studies were based on data from a home setting ( Cross et al., 2015 ; Koo et al., 2015 ; Spears et al., 2015 ; Boyle et al., 2016 ; de Vries et al., 2016 ; Harbard et al., 2016 ; Barry et al., 2017 ; Frison and Eggermont, 2017 ; Oberst et al., 2017 ; Yan et al., 2017 ; Booker et al., 2018 ; Marques et al., 2018 ; Wartberg et al., 2018 ; Critchlow et al., 2019 ). Eleven publications were reviews or meta-analyses and included primary studies from different settings ( Aboujaoude et al., 2015 ; Best et al., 2015 ; Hamm et al., 2015 ; Richards et al., 2015 ; Fisher et al., 2016 ; Foody et al., 2017 ; Marchant et al., 2017 ; Curtis et al., 2018 ; Erfani and Abedin, 2018 ; John et al., 2018 ; Memon et al., 2018 ). One study used both a home and school setting ( Erreygers et al., 2018 ), and 11 (14%) of the included studies did not mention the study setting for data collection ( Ferguson et al., 2014 ; Tseng and Yang, 2015 ; Fahy et al., 2016 ; Burnette et al., 2017 ; Jafarpour et al., 2017 ; Przybylski and Weinstein, 2017 ; Wolke et al., 2017 ; O’Reilly et al., 2018 ; Twenge et al., 2018 ; Throuvala et al., 2019 ; Twenge and Campbell, 2019 ).

Mental Health Foci of Included Studies

For a visual overview of the mental health foci of the included studies see Figures 2 , ​ ,3. 3 . Most studies had a focus on different negative aspects of mental health, as evident from the frequently used terms in Figures 2 , ​ ,3. 3 . The most studied aspect was depression, with 23 (29%) studies examining the relationship between social media use and depressive symptoms ( Ferguson et al., 2014 ; Neira and Barber, 2014 ; O’Connor et al., 2014 ; Banjanin et al., 2015 ; Richards et al., 2015 ; Spears et al., 2015 ; Tseng and Yang, 2015 ; Fahy et al., 2016 ; Frison and Eggermont, 2016 , 2017 ; Woods and Scott, 2016 ; Banyai et al., 2017 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Larm et al., 2017 ; Nesi et al., 2017a ; Salmela-Aro et al., 2017 ; Fredrick and Demaray, 2018 ; Houghton et al., 2018 ; Niu et al., 2018 ; Twenge et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ). Twenty of the included studies focused on different aspects of good mental health, such as well-being, happiness, or quality of life ( Best et al., 2014 , 2015 ; Bourgeois et al., 2014 ; Ferguson et al., 2014 ; Cross et al., 2015 ; Koo et al., 2015 ; Richards et al., 2015 ; Spears et al., 2015 ; Fahy et al., 2016 ; Foerster and Roosli, 2017 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Yan et al., 2017 ; Booker et al., 2018 ; de Lenne et al., 2018 ; Erfani and Abedin, 2018 ; Erreygers et al., 2018 ; Lai et al., 2018 ; van den Eijnden et al., 2018 ; Twenge and Campbell, 2019 ). Nineteen studies had a more broad-stroke approach, and covered general mental health or psychiatric problems ( Aboujaoude et al., 2015 ; Hanprathet et al., 2015 ; Hase et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Spears et al., 2015 ; Fisher et al., 2016 ; Barry et al., 2017 ; Jafarpour et al., 2017 ; Kim, 2017 ; Merelle et al., 2017 ; Oberst et al., 2017 ; Wolke et al., 2017 ; Marengo et al., 2018 ; Marques et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Holfeld and Mishna, 2019 ; Kim et al., 2019 ; Larm et al., 2019 ). Eight studies examined the link between social media use and body dissatisfaction and eating disorder symptoms ( Ferguson et al., 2014 ; Meier and Gray, 2014 ; de Vries et al., 2016 ; Burnette et al., 2017 ; Rousseau et al., 2017 ; Tiggemann and Slater, 2017 ; Marengo et al., 2018 ; Wartberg et al., 2018 ). Anxiety was the focus of seven studies ( O’Connor et al., 2014 ; Koo et al., 2015 ; Spears et al., 2015 ; Fahy et al., 2016 ; Woods and Scott, 2016 ; Colder Carras et al., 2017 ; Yan et al., 2017 ), and 13 studies included a focus on the relationship between alcohol use and social media use ( O’Connor et al., 2014 ; Boyle et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Brunborg et al., 2017 ; Geusens and Beullens, 2017 , 2018 ; Larm et al., 2017 ; Merelle et al., 2017 ; Nesi et al., 2017b ; Curtis et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Critchlow et al., 2019 ; Kim et al., 2019 ). Seven studies examined the effect of social media use on sleep ( Harbard et al., 2016 ; Woods and Scott, 2016 ; Yan et al., 2017 ; Nursalam et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Larm et al., 2019 ). Five studies saw how drug use and social media use affected each other ( O’Connor et al., 2014 ; Merelle et al., 2017 ; Sampasa-Kanyinga et al., 2018 ; Kim et al., 2019 ; Larm et al., 2019 ). Self-harm and suicidal behavior was the focus of eleven studies ( O’Connor et al., 2014 ; Sampasa-Kanyinga and Lewis, 2015 ; Tseng and Yang, 2015 ; Kim, 2017 ; Marchant et al., 2017 ; Merelle et al., 2017 ; Fredrick and Demaray, 2018 ; John et al., 2018 ; Memon et al., 2018 ; Twenge et al., 2018 ; Kim et al., 2019 ). Other areas of focus other than the aforementioned are loneliness, self-esteem, fear of missing out and other non-pathological measures ( Neira and Barber, 2014 ; Banyai et al., 2017 ; Barry et al., 2017 ; Colder Carras et al., 2017 ).

Social Media Metrics of Included Studies

The studies included in the current scoping review often focus on specific, widely used, social media and social networking services, such as 31 (39%) studies focusing on Facebook ( Bourgeois et al., 2014 ; Meier and Gray, 2014 ; Banjanin et al., 2015 ; Cross et al., 2015 ; Hanprathet et al., 2015 ; Richards et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Spears et al., 2015 ; Boyle et al., 2016 ; de Vries et al., 2016 ; Frison and Eggermont, 2016 ; Harbard et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Banyai et al., 2017 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Larm et al., 2017 ; Merelle et al., 2017 ; Nesi et al., 2017a , b ; Rousseau et al., 2017 ; Tiggemann and Slater, 2017 ; Booker et al., 2018 ; de Lenne et al., 2018 ; Lai et al., 2018 ; Marengo et al., 2018 ; Marques et al., 2018 ; Memon et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Settanni et al., 2018 ; Twenge et al., 2018 ), 11 on Instagram ( Sampasa-Kanyinga and Lewis, 2015 ; Boyle et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Frison and Eggermont, 2017 ; Nesi et al., 2017a ; Marengo et al., 2018 ; Memon et al., 2018 ; Sampasa-Kanyinga et al., 2018 ), 11 including Twitter ( Richards et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Spears et al., 2015 ; Harbard et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Merelle et al., 2017 ; Nesi et al., 2017a ; Memon et al., 2018 ; Sampasa-Kanyinga et al., 2018 ), and five studies asking about Snapchat ( Boyle et al., 2016 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Nesi et al., 2017a ; Marengo et al., 2018 ). Eight studies mentioned Myspace ( Richards et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; de Vries et al., 2016 ; Harbard et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Larm et al., 2017 ; Booker et al., 2018 ; Sampasa-Kanyinga et al., 2018 ) and two asked about Tumblr ( Barry et al., 2017 ; Nesi et al., 2017a ). Other media such as Skype ( Merelle et al., 2017 ), Youtube ( Richards et al., 2015 ), WhatsApp ( Brunborg et al., 2017 ), Ping ( Merelle et al., 2017 ), Bebo ( Booker et al., 2018 ), Hyves ( de Vries et al., 2016 ), Kik ( Brunborg et al., 2017 ), Ask ( Brunborg et al., 2017 ), and Qzone ( Niu et al., 2018 ) were only included in one study each.

Almost half ( n = 34, 43%) of the included studies focus on use of social network sites or online communication in general, without specifying particular social media sites, leaving this up to the study participants to decide ( Best et al., 2014 , 2015 ; Ferguson et al., 2014 ; Neira and Barber, 2014 ; O’Connor et al., 2014 ; Koo et al., 2015 ; Tseng and Yang, 2015 ; Fahy et al., 2016 ; Woods and Scott, 2016 ; Burnette et al., 2017 ; Colder Carras et al., 2017 ; Foerster and Roosli, 2017 ; Foody et al., 2017 ; Geusens and Beullens, 2017 , 2018 ; Jafarpour et al., 2017 ; Kim, 2017 ; Marchant et al., 2017 ; Oberst et al., 2017 ; Przybylski and Weinstein, 2017 ; Salmela-Aro et al., 2017 ; Yan et al., 2017 ; Curtis et al., 2018 ; Erfani and Abedin, 2018 ; Erreygers et al., 2018 ; Nursalam et al., 2018 ; Scott and Woods, 2018 ; van den Eijnden et al., 2018 ; Wartberg et al., 2018 ; Critchlow et al., 2019 ; Holfeld and Mishna, 2019 ; Larm et al., 2019 ; Throuvala et al., 2019 ; Twenge and Campbell, 2019 ). Seven of the included studies examined the relationship between virtual game worlds or socially oriented video games and mental health ( Ferguson et al., 2014 ; Best et al., 2015 ; Spears et al., 2015 ; Yan et al., 2017 ; van den Eijnden et al., 2018 ; Larm et al., 2019 ; Twenge and Campbell, 2019 ).

In the 79 studies included in this scoping review, several approaches to measuring social media use are utilized. The combination of frequency and duration of social media use is by far the most used measurement of social media use, and 44 (56%) of the included studies collected data on these parameters ( Ferguson et al., 2014 ; Meier and Gray, 2014 ; Neira and Barber, 2014 ; Banjanin et al., 2015 ; Best et al., 2015 ; Hanprathet et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Tseng and Yang, 2015 ; Boyle et al., 2016 ; de Vries et al., 2016 ; Frison and Eggermont, 2016 , 2017 ; Harbard et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Woods and Scott, 2016 ; Banyai et al., 2017 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Foerster and Roosli, 2017 ; Jafarpour et al., 2017 ; Kim, 2017 ; Larm et al., 2017 , 2019 ; Merelle et al., 2017 ; Nesi et al., 2017b ; Oberst et al., 2017 ; Rousseau et al., 2017 ; Tiggemann and Slater, 2017 ; Yan et al., 2017 ; Booker et al., 2018 ; de Lenne et al., 2018 ; Erreygers et al., 2018 ; Houghton et al., 2018 ; Lai et al., 2018 ; Marengo et al., 2018 ; Marques et al., 2018 ; Niu et al., 2018 ; Nursalam et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; Twenge et al., 2018 ; van den Eijnden et al., 2018 ; Twenge and Campbell, 2019 ). Eight studies focused on the relationship between social media addiction or excessive use and mental health ( Banjanin et al., 2015 ; Tseng and Yang, 2015 ; Banyai et al., 2017 ; Merelle et al., 2017 ; Nursalam et al., 2018 ; Settanni et al., 2018 ; Wang et al., 2018 ). Bergen Social Media Addiction Scale is a commonly used questionnaire amongst the included studies ( Hanprathet et al., 2015 ; Banyai et al., 2017 ; Settanni et al., 2018 ). Seven studies asked about various specific actions on social media, such as liking or commenting on photos, posting something or participating in a discussion ( Meier and Gray, 2014 ; Koo et al., 2015 ; Nesi et al., 2017b ; Geusens and Beullens, 2018 ; Marques et al., 2018 ; van den Eijnden et al., 2018 ; Critchlow et al., 2019 ).

Five studies had a specific and sole focus on the link between social media use and alcohol, and examined how various alcohol-related social media use affected alcohol intake ( Boyle et al., 2016 ; Geusens and Beullens, 2017 , 2018 ; Nesi et al., 2017b ; Critchlow et al., 2019 ). Some studies had a more theory-based focus and investigated themes such as peer comparison, social media intrusion or pro-social behavior on social media and its effect on mental health ( Bourgeois et al., 2014 ; Rousseau et al., 2017 ; de Lenne et al., 2018 ). One of the included studies looked into night-time specific social media use ( Scott and Woods, 2018 ) and one looked into pre-bedtime social media behavior ( Harbard et al., 2016 ) to study the link between this use and sleep.

Amongst the 79 included studies, only six (8%) studies had participants of one gender ( Ferguson et al., 2014 ; Meier and Gray, 2014 ; Best et al., 2015 ; Burnette et al., 2017 ; Jafarpour et al., 2017 ; Tiggemann and Slater, 2017 ). Sixteen studies (20%) did not mention the gender distribution of the participants ( Aboujaoude et al., 2015 ; Best et al., 2015 ; Hamm et al., 2015 ; Richards et al., 2015 ; Fisher et al., 2016 ; Woods and Scott, 2016 ; Foody et al., 2017 ; Marchant et al., 2017 ; Przybylski and Weinstein, 2017 ; Curtis et al., 2018 ; Erfani and Abedin, 2018 ; John et al., 2018 ; Memon et al., 2018 ; O’Reilly et al., 2018 ; Twenge et al., 2018 ; Twenge and Campbell, 2019 ). Several of these were meta-analyses or reviews ( Aboujaoude et al., 2015 ; Best et al., 2014 ; Curtis et al., 2018 ; Foody et al., 2017 ; John et al., 2018 ; Erfani and Abedin, 2018 ; Wallaroo, 2020 ). The studies that included both genders as participants generally had a well-balanced gender distribution with no gender below 40% of the participants. Eight of the studies did not report gender-specific results ( Harbard et al., 2016 ; Nesi et al., 2017b ; Curtis et al., 2018 ; de Lenne et al., 2018 ; Niu et al., 2018 ; Nursalam et al., 2018 ; Wang et al., 2018 ; Twenge and Campbell, 2019 ). Of the included studies, gender was seldom examined as an explanatory variable, and other sociodemographic variables (e.g., ethnicity, socioeconomic status) were not included at all.

Implicit Causation Based on Direction of Association

Sixty-one (77%) of the included studies has social media use as the independent variable and some of the mentioned measurements of mental health as the dependent variable ( Aboujaoude et al., 2015 ; Banjanin et al., 2015 ; Banyai et al., 2017 ; Barry et al., 2017 ; Best et al., 2014 ; Booker et al., 2018 ; Bourgeois et al., 2014 ; Boyle et al., 2016 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Critchlow et al., 2019 ; Cross et al., 2015 ; Curtis et al., 2018 ; de Lenne et al., 2018 ; de Vries et al., 2016 ; Erfani and Abedin, 2018 ; Fahy et al., 2016 ; Fisher et al., 2016 ; Foerster and Roosli, 2017 ; Fredrick and Demaray, 2018 ; Frison and Eggermont, 2016 ; Geusens and Beullens, 2018 ; Hamm et al., 2015 ; Hanprathet et al., 2015 ; Harbard et al., 2016 ; Hase et al., 2015 ; Holfeld and Mishna, 2019 ; Jafarpour et al., 2017 ; John et al., 2018 ; Kim et al., 2019 ; Kim, 2017 ; Lai et al., 2018 ; Larm et al., 2017 , 2019 ; Marengo et al., 2018 ; Marques et al., 2018 ; Meier and Gray, 2014 ; Memon et al., 2018 ; Neira and Barber, 2014 ; Nesi et al., 2017b ; Niu et al., 2018 ; Nursalam et al., 2018 ; O’Connor et al., 2014 ; O’Reilly et al., 2018 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Richards et al., 2015 ; Sampasa-Kanyinga and Chaput, 2016 ; Sampasa-Kanyinga and Lewis, 2015 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Spears et al., 2015 ; Tseng and Yang, 2015 ; Twenge and Campbell, 2019 ; Twenge et al., 2018 ; van den Eijnden et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ; Wolke et al., 2017 ; Woods and Scott, 2016 ; Yan et al., 2017 ). Most of the included studies hypothesize social media use pattern will affect youth mental health in certain ways. The majority of the included studies tend to find a correlation between more frequent social media use and poor well-being and/or mental health (see Supplementary Table 2 ). The strength of this correlation is however heterogeneous as social media use is measured substantially different across studies. Four (5%) of the included studies focus explicitly on how mental health can affect social media use ( Merelle et al., 2017 ; Nesi et al., 2017a ; Erreygers et al., 2018 ; Settanni et al., 2018 ). Fourteen studies included a mediating factor or focus on reciprocal relationships between social media use and mental health ( Ferguson et al., 2014 ; Koo et al., 2015 ; Tseng and Yang, 2015 ; Frison and Eggermont, 2017 ; Geusens and Beullens, 2017 ; Marchant et al., 2017 ; Oberst et al., 2017 ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Tiggemann and Slater, 2017 ; Houghton et al., 2018 ; Marques et al., 2018 ; Niu et al., 2018 ; Wang et al., 2018 ). An example is a cross-sectional study by Ferguson et al. (2014) suggesting that exposure to social media contribute to later peer competition which was found to be a predictor of negative mental health outcomes such as eating disorder symptoms.

Cyberbullying as a Nexus

Thirteen of the 79 (17%) included studies investigated cyberbullying as the measurement of social media use ( Aboujaoude et al., 2015 ; Cross et al., 2015 ; Hamm et al., 2015 ; Hase et al., 2015 ; Spears et al., 2015 ; Fahy et al., 2016 ; Fisher et al., 2016 ; Foody et al., 2017 ; Przybylski and Bowes, 2017 ; Wolke et al., 2017 ; Fredrick and Demaray, 2018 ; John et al., 2018 ; Holfeld and Mishna, 2019 ). Most of the systematic reviews and meta-analyses included focused on cyberbullying. A cross-sectional study from 2017 suggests that cyberbullying has similar negative effects as direct or relational bullying, and that cyberbullying is “mainly a new tool to harm victims already bullied by traditional means” ( Wolke et al., 2017 ). A meta-analysis from 2016 concludes that “peer cybervictimization is indeed associated with a variety of internalizing and externalizing problems among adolescents” ( Fisher et al., 2016 ). A systematic review from 2018 concludes that both victims and perpetrators of cyberbullying are at greater risk of suicidal behavior compared with non-victims and non-perpetrators ( John et al., 2018 ).

Strengths and Limitations of Present Study

The main strength of this scoping review lies in the effort to give a broad overview of published research related to use of social media, and mental health and well-being among adolescents. Although a range of reviews on screen-based activities in general and mental health and well-being exist ( Dickson et al., 2018 ; Orben, 2020 ), they do not necessarily discern between social media use and other types of technology-based media. Also, some previous reviews tend to be more particular regarding mental health outcome ( Best et al., 2014 ; Seabrook et al., 2016 ; Orben, 2020 ), or do not focus on adolescents per se ( Seabrook et al., 2016 ). The main limitation is that, despite efforts to make the search strategy as comprehensive and inclusive as possible, we probably have not been able to identify all relevant studies – this is perhaps especially true when studies do include relevant information about social media and mental health/well-being, but this information is part of sub-group analyses or otherwise not the main aim of the studies. In a similar manner, related to qualitative studies, we do not know if our search strategy were as efficient in identifying studies of relevance if this was not the main theme or focus of the study. Despite this, we believe that we were able to strike a balance between specificity and sensitivity in our search strategy.

Description of Central Themes and Core Concepts

The findings from the present scoping review on social media use and mental health and well-being among adolescents revealed that the majority (about 3/4) of the included studies focused on social media and pathology. The core concepts identified are social media use and its statistical association with symptoms of depression, general psychiatric symptoms and other symptoms of psychopathology. Similar findings were made by Keles et al. (2020) in a systematic review from 2019. Focus on the potential association between social media use and positive outcomes seems to be rarer in the current literature, even though some studies focused on well-being which also includes positive aspects of mental health. Studies focusing on screen-based media in general and well-being is more prevalent than studies linking social media specifically with well-being ( Orben, 2020 ). The notion that excessive social media use is associated with poor mental health is well established within mainstream media. Our observation that this preconception seems to be the starting point for much research is not conducive to increased knowledge, but also alluded to elsewhere ( Coyne et al., 2020 ).

Why the Focus on Poor Mental Health/Pathology?

The relationship between social media and mental health is likely to be complex, and social media use can be beneficial for maintaining friendships and enriching social life ( Seabrook et al., 2016 ; Birkjær and Kaats, 2019 ; Coyne et al., 2020 ; Orben, 2020 ). This scoping review reveals that the majority of studies focusing on effects of social media use has a clearly stated focus on pathology and detrimental results of social media use. Mainstream media and the public discourse has contributed in creating a culture of fear around social media, with a focus on its negative elements ( Ahn, 2012 ; O’Reilly et al., 2018 ). It is difficult to pin-point why the one-sided focus on the negative effects of social media has been established within the research literature. But likely reasons are elements of “moral panic,” and reports of increases in mental health problems among adolescents in the same period that social media were introduced and became wide-spread ( Birkjær and Kaats, 2019 ). The phenomenon of moral panic typically resurges with the introduction and increasing use of new technologies, as happened with video games, TV, and radio ( Mueller, 2019 ).

The Metrics of Social Media

Social media trends change rapidly, and it is challenging for the research field to keep up. The included studies covered some of the most frequently used social media, but the amount of studies focusing on each social media did not accurately reflect the contemporary distribution of users. Even though sites such as Instagram and Snapchat were covered in some studies, the coverage did not do justice to the amount of users these sites had. Newer social media sites such as TikTok were not mentioned in the included studies even though it has several hundred million daily users ( Mediakix, 2019 ; Wallaroo, 2020 ).

Across the included studies there was some variation in how social media were gauged, but the majority of studies focused on the mere frequency and duration of use. There were little focus on separating between different forms of (inter)actions on social media, as these can vary between being a victim of cyberbullying to participating in healthy community work. Also, few studies differentiated between types of actions (i.e., posting, scrolling, reading), active and passive modes of social media use (i.e., production versus consumption, and level of interactivity), a finding similar to other reports ( Seabrook et al., 2016 ; Verduyn et al., 2017 ; Orben, 2020 ). There is reason to believe that different modes of use on social media platforms are differentially associated with mental health, and a recent narrative review highlight the need to address this in future research ( Orben, 2020 ). One of the included studies found for instance that it is not the total time spent on Facebook or the internet, but the specific amount of time allocated to photo-related activities that is associated with greater symptoms of eating disorders such as thin ideal internalization, self-objectification, weight dissatisfaction, and drive for thinness ( Meier and Gray, 2014 ). This observation can possibly be explained by social comparison mechanisms ( Appel et al., 2016 ) and passive use of social media ( Verduyn et al., 2017 ). The lack of research differentiating social media use and its association with mental health is an important finding of this scoping review and will hopefully contribute to this being included in future studies.

Few studies examined the motivation behind choosing to use social media, or the mental health status of the users when beginning a social media session. It has been reported that young people sometimes choose to enter sites such as Facebook and Twitter as an escape from threats to their mental health such as experiencing overwhelming pressure in daily life ( Boyd, 2014 ). This kind of escapism can be explained through uses and gratifications theory [see for instance ( Coyne et al., 2020 )]. On the other hand, more recent research suggest that additional motivational factors may include the need to control relationships, content, presentation, and impressions ( Throuvala et al., 2019 ), and it is possible that social media use can act as an reinforcement of adolescents’ current moods and motivations ( Birkjær and Kaats, 2019 ). Regardless, it seems obvious that the interplay between online and offline use and underlying motivational mechanisms needs to be better understood.

There has also been some questions about the accuracy when it comes to deciding the amount and frequency of one’s personal social media use. Without measuring duration and frequency of use directly and objectively it is unlikely that subjective self-report of general use is reliable ( Kobayashi and Boase, 2012 ; Scharkow, 2016 , 2019 ; Naab et al., 2019 ). Especially since the potential for social media use is almost omnipresent and the use itself is diverse in nature. Also, due to processes such as social desirability, it is likely that some participants report lower amounts of social media use as excessive use is seen largely undesirable ( Krumpal, 2013 ). Inaccurate reporting of prior social media use could also be a threat to the validity of the reported numbers and thus bias the results reported. Real-time tracking of actual use and modes of use is therefore recommended in future studies to ensure higher accuracy of these aspects of social media use ( Coyne et al., 2020 ; Orben, 2020 ), despite obvious legal and ethical challenges. Another aspect of social media use which does not seem to be addressed is potential spill-over effects, where use of social media leads to potential interest in or thinking about use of – and events or contents on – social media when the individual is offline. When this aspect has been addressed, it seems to be in relation to preoccupations and with a focus on excessive use or addictive behaviors ( Griffiths et al., 2014 ). Conversely, given the ubiquitous and important role of social media, experiences on social media – for better or for worse – are likely to be interconnected with the rest of an individual’s lived experience ( Birkjær and Kaats, 2019 ).

The Studies Seem to Implicitly Think That the Use of Social Media “Causes”/“Affects” Mental Health (Problems)

Most of the included studies establish an implicit causation between social media and mental health. It is assumed that social media use has an impact on mental health. The majority of studies included establish some correlation between more frequent use of social media and poor well-being/mental health, as evident from Supplementary Table 2 . As formerly mentioned, most of the included studies are cross-sectional and cannot shed light into temporality or cause-and-effect. In total, only 16 studies had a longitudinal design, using different types of regression models, latent growth curve models and cross-lagged models. Yet there seems to be an unspoken expectation that the direction of the association is social media use affecting mental health. The reason for this supposition is unclear, but again it is likely that the mainstream media discourse dominated by mostly negative stories and reports of social media use has some impact together with the observed moral panic.

With the increased popularity of social media and internet arrived a reduction of face-to-face contact and supposed increased social isolation ( Kraut et al., 1998 ; Espinoza and Juvonen, 2011 ). This view is described as the displacement hypothesis [see for instance ( Coyne et al., 2020 )]. Having a thriving social life and community with meaningful relations are for many considered vital for well-being and good mental health, and the supposed reduction of sociality were undoubtedly met with skepticism by some. Social media use has increased rapidly among young people over the last two decades along with reports that mental health problems are increasing. Several studies report that there is a rising prevalence of symptom of anxiety and depression among our adolescents ( Bor et al., 2014 ; Olfson et al., 2015 ). The observation that increases in social media use and mental health issues happened in more or less the same time period can have contributed to focus on how use of social media affects mental health problems.

The existence of an implicit causation is supported by the study variables chosen and the lack of positively worded outcomes. Depression, anxiety, alcohol use, psychiatric problems, suicidal behavior and eating disorders are amongst the most studied outcome-variables. On the other side of the spectrum we have well-being, which can oscillate from positive to negative, whilst the measures of pathology only vary from “ill” to “not ill” with positive outcomes not possible.

What Is the Gap in the Literature?

The current literature on social media and mental health among youth is still developing and has several gaps and shortcomings, as evident from this scoping review and other publications ( Seabrook et al., 2016 ; Coyne et al., 2020 ; Keles et al., 2020 ; Orben, 2020 ). Some of the gaps and shortcomings in the field we propose solutions for has been identified in a systematic review from 2019 by Keles et al. (2020) . The majority of the included studies in the current scoping review were cross-sectional, were limited in their inclusion of potential confounders and 3rd variables such as sociodemographics and personality, preventing knowledge about possible cause-and-effect between social media and mental health. There is a lack of longitudinal studies examining the effects of social media over extended periods of time, as well as investigations longitudinally of how mental health impacts social media use. However, since the formal search was ended for this scoping review, some innovative studies have emerged using longitudinal data ( Brunborg and Andreas, 2019 ; Orben et al., 2019 ; Coyne et al., 2020 ). More high quality longitudinal studies of social media use and mental health could help us identify the patterns over time and help us learn about possible cause-and-effect relationships, as well as disentangling between- and within-person associations ( Coyne et al., 2020 ; Orben, 2020 ). Furthermore, both social media use and mental health are complex phenomena in themselves, and future studies need to consider which aspects they want to investigate when trying to understand their relationship. Mechanisms linking social media use and eating disorders are for instance likely to be different than mechanisms linking social media use and symptoms of ADHD.

Our literature search also revealed a paucity of qualitative studies exploring the why’s and how’s of social media use in relation to mental health among adolescents. Few studies examine how youth themselves experience and perceive the relationship between social media and mental health, and the reasons for their continued and frequent use. Qualitatively oriented studies would contribute to a deeper understanding of adolescent’s social media sphere, and their thoughts about the relationship between social media use and mental health [see for instance ( Burnette et al., 2017 )]. For instance, O’Reilly et al. (2018) found that adolescents viewed social media as a threat to mental well-being, and concluded that they buy into the idea that “inherently social media has negative effects on mental wellbeing” and seem to “reify the moral panic that has become endemic to contemporary discourses.” On the other hand, Weinstein found using both quantitative and qualitative data that adolescents’ perceptions of the relationship between social media use and well-being probably is more nuanced, and mostly positive. Another clear gap in the research literature is the lack of focus on potentially positive aspects of social media use. It is obvious that there are some positive sides of the use of social media, and these also need to be investigated further ( Weinstein, 2018 ; Birkjær and Kaats, 2019 ). Gender-specific analyses are also lacking in the research literature, and there is reason to believe that social media use have different characteristics between the genders with different relationships to mental health. In fact, recent findings indicate that not only gender should be considered an important factor when investigating the role of social media in adolescents’ lives, but individual characteristics in general ( Orben et al., 2019 ; Orben, 2020 ). Analyses of socioeconomic status and geographic location are also lacking and it is likely that these factors might play a role the potential association between social media use and mental health. And finally, several studies point to the fact that social media potentially could be a fruitful arena for promoting mental well-being among youth, and developing mental health literacy to better equip our adolescents for the challenges that will surely arise ( O’Reilly et al., 2018 ; Teesson et al., 2020 ).

Research into the association between social media use and mental health and well-being among adolescents is rapidly emerging. The field is characterized by a focus on the association between social media use and negative aspects of mental health and well-being, and where studies focusing on the potentially positive aspects of social media use are lacking. Presently, the majority of studies in the field are quantitatively oriented, with most utilizing a cross-sectional design. An increase in qualitatively oriented studies would add to the field of research by increasing the understanding of adolescents’ social-media life and their own experiences of its association with mental health and well-being. More studies using a longitudinal design would contribute to examining the effects of social media over extended periods of time and help us learn about possible cause-and-effect relationships. Few studies look into individual factors, which may be important for our understanding of the association. Social media use and mental health and well-being are complex phenomena, and future studies could benefit from specifying the type of social media use they focus on when trying to understand its link to mental health. In conclusion, studies including more specific aspects of social media, individual differences and potential intermediate variables, and more studies using a longitudinal design are needed as the research field matures.

Author Contributions

JS conceptualized the review approach and provided general guidance to the research team. VS and JS drafted the first version of this manuscript. JS, GH, and LA developed the draft further based on feedback from the author group. All authors reviewed and approved the final version of the manuscript and have made substantive intellectual contributions to the development of this manuscript.

Conflict of Interest

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

Acknowledgments

We would like to thank Bergen municipality, Hordaland County Council and Western Norway University of Applied Sciences for their collaboration and help with the review. We would also like to thank Senior Librarian Marita Heinz at the Norwegian Institute for Public Health for vital help conducting the literature search.

Funding. This review was partly funded by Regional Research Funds in Norway, funding #RFF297031. No other specific funding was received for the present project. The present project is associated with a larger innovation-project lead by Bergen municipality in Western Norway related to the use of social media and mental health and well-being. The innovation-project is funded by a program initiated by the Norwegian Directorate of Health, and in Vestland county coordinated by the County Council (County Authority). The project aims to explore social media as platform for health-promotion among adolescents.

1 https://rayyan.qcri.org/welcome

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2020.01949/full#supplementary-material

  • Aboujaoude E., Savage M. W., Starcevic V., Salame W. O. (2015). Cyberbullying: review of an old problem gone viral. J. Adolesc. Health 57 10–18. 10.1016/j.jadohealth.2015.04.011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Ahn J. (2012). Teenagers’ experiences with social network sites: relationships to bridging and bonding social capital. Inform. Soc. 28 99–109. 10.1080/01972243.2011.649394 [ CrossRef ] [ Google Scholar ]
  • Appel H., Gerlach A. L., Crusius J. (2016). The interplay between Facebook use, social comparison, envy, and depression. Curr. Opin. Psychol. 9 44–49. 10.1016/j.copsyc.2015.10.006 [ CrossRef ] [ Google Scholar ]
  • Arksey H., O’Malley L. (2005). Scoping studies: towards a methodological framework. Int. J. Soc. Res. Methodol. 8 19–32. 10.1080/1364557032000119616 [ CrossRef ] [ Google Scholar ]
  • Banjanin N., Banjanin N., Dimitrijevic I., Pantic I. (2015). Relationship between internet use and depression: focus on physiological mood oscillations, social networking and online addictive behavior. Comput. Hum. Behav. 43 308–312. 10.1016/j.chb.2014.11.013 [ CrossRef ] [ Google Scholar ]
  • Banyai F., Zsila A., Kiraly O., Maraz A., Elekes Z., Griffiths M. D., et al. (2017). Problematic social media use: results from a large-scale nationally representative adolescent sample. PLoS One 12 : e0169839 . 10.1371/journal.pone.0169839 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Barry C. T., Sidoti C. L., Briggs S. M., Reiter S. R., Lindsey R. A. (2017). Adolescent social media use and mental health from adolescent and parent perspectives. J. Adolesc. 61 1–11. 10.1016/j.adolescence.2017.08.005 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Bell V., Bishop D. V., Przybylski A. K. (2015). The debate over digital technology and young people. BMJ 351 : h3064 . 10.1136/bmj.h3064 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Best P., Manktelow R., Taylor B. (2014). Online communication, social media and adolescent wellbeing: a systematic narrative review. Child. Youth Serv. Rev. 41 27–36. 10.1016/j.childyouth.2014.03.001 [ CrossRef ] [ Google Scholar ]
  • Best P., Taylor B., Manktelow R. (2015). I’ve 500 friends, but who are my mates? Investigating the influence of online friend networks on adolescent wellbeing. J. Public Ment. Health 14 135–148. 10.1108/jpmh-05-2014-0022 [ CrossRef ] [ Google Scholar ]
  • Birkjær M., Kaats M. (2019). in Er sociale Medier Faktisk en Truss for Unges Trivsel? [Does Social Media Really Pose a Threat to Young People’s Well-Being?] , ed. N.M.H.R. Institute (København: Nordic Co-operation; ). [ Google Scholar ]
  • Booker C. L., Kelly Y. J., Sacker A. (2018). Gender differences in the associations between age trends of social media interaction and well-being among 10-15 year olds in the UK. BMC Public Health 18 : 321 . 10.1186/s12889-018-5220-4 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Bor W., Dean A. J., Najman J., Hayatbakhsh R. (2014). Are child and adolescent mental health problems increasing in the 21st century? A systematic review. Austr. N. Z. J. Psychiatry 48 606–616. 10.1177/0004867414533834 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Bourgeois A., Bower J., Carroll A. (2014). Social networking and the social and emotional wellbeing of adolescents in Australia. J. Psychol. Counsell. Sch. 24 167–182. 10.1017/jgc.2014.14 [ CrossRef ] [ Google Scholar ]
  • Boyd D. (2014). It’s Complicated: The Social Lives of Networked Teens. New Haven, CT: Yale University Press. [ Google Scholar ]
  • Boyle S. C., LaBrie J. W., Froidevaux N. M., Witkovic Y. D. (2016). Different digital paths to the keg? How exposure to peers’ alcohol-related social media content influences drinking among male and female first-year college students. Addict. Behav. 57 21–29. 10.1016/j.addbeh.2016.01.011 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Brunborg G. S., Andreas J. B. (2019). Increase in time spent on social media is associated with modest increase in depression, conduct problems, and episodic heavy drinking. J. Adolesc. 74 201–209. 10.1016/j.adolescence.2019.06.013 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Brunborg G. S., Andreas J. B., Kvaavik E. (2017). Social media use and episodic heavy drinking among adolescents. Psychol. Rep. 120 475–490. 10.1177/0033294117697090 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Burnette C. B., Kwitowski M. A., Mazzeo S. E. (2017). “I don’t need people to tell me I’m pretty on social media:” A qualitative study of social media and body image in early adolescent girls. Body Image 23 114–125. 10.1016/j.bodyim.2017.09.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Childnet International (2018). Age Restrictions on Social Media Services. Available online at: https://www.childnet.com/blog/age-restrictions-on-social-media-services (accessed September 30, 2019). [ Google Scholar ]
  • Colder Carras M., Van Rooij A. J., Van de Mheen D., Musci R., Xue Q., Mendelson T. (2017). Video gaming in a hyperconnected world: a cross-sectional study of heavy gaming, problematic gaming symptoms, and online socializing in adolescents. Comput. Hum. Bahav. 68 472–479. 10.1016/j.chb.2016.11.060 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Coyne S. M., Rogers A. A., Zurcher J. D., Stockdale L., Booth M. (2020). Does time spent using social media impact mental health?: an eight year longitudinal study. Comput. Hum. Behav. 104 : 106160 10.1016/j.chb.2019.106160 [ CrossRef ] [ Google Scholar ]
  • Critchlow N., MacKintosh A. M., Hooper L., Thomas C., Vohra J. (2019). Participation with alcohol marketing and user-created promotion on social media, and the association with higher-risk alcohol consumption and brand identification among adolescents in the UK. Addict. Res. Theory 27 515–526. 10.1080/16066359.2019.1567715 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Cross D., Lester L., Barnes A. (2015). A longitudinal study of the social and emotional predictors and consequences of cyber and traditional bullying victimisation. Int. J. Public Health 60 207–217. 10.1007/s00038-015-0655-1 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Curtis B. L., Lookatch S. J., Ramo D. E., McKay J. R., Feinn R. S., Kranzler H. R. (2018). Meta-analysis of the association of alcohol-related social media use with alcohol consumption and alcohol-related problems in adolescents and young adults. Alcohol. Clin. Exp. Res. 42 978–986. 10.1111/acer.13642 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • de Lenne O., Vandenbosch L., Eggermont S., Karsay K., Trekels J. (2018). Picture-perfect lives on social media: a cross-national study on the role of media ideals in adolescent well-being. Med. Psychol. 23 52–78. 10.1080/15213269.2018.1554494 [ CrossRef ] [ Google Scholar ]
  • de Vries D. A., Peter J., de Graaf H., Nikken P. (2016). Adolescents’ social network site use, peer appearance-related feedback, and body dissatisfaction: testing a mediation model. J. Youth Adolesc. 45 211–224. 10.1007/s10964-015-0266-4 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Dickson K., Richardson M., Kwan I., MacDowall W., Burchett H., Stansfield C., et al. (2018). Screen-Based Activities and Children and Young People’s Mental Health: A Systematic Map of Reviews. London: University College London. [ Google Scholar ]
  • Erfani S. S., Abedin B. (2018). Impacts of the use of social network sites on users’ psychological well-being: a systematic review. J. Assoc. Inform. Sci. Technol. 69 900–912. 10.1002/asi.24015 [ CrossRef ] [ Google Scholar ]
  • Erreygers S., Vandebosch H., Vranjes I., Baillien E., De Witte H. (2018). Feel good, do good online? Spillover and crossover effects of happiness on adolescents’ online prosocial behavior. Happiness Stud. 20 1241–1258. 10.1007/s10902-018-0003-2 [ CrossRef ] [ Google Scholar ]
  • Espinoza G., Juvonen J. (2011). The pervasiveness, connectedness, and intrusiveness of social network site use among young adolescents. Cyberpsychol. Behav. Soc. Netw. 14 705–709. 10.1089/cyber.2010.0492 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Fahy A. E., Stansfield S. A., Smuk M., Smith N. R., Cummins S., Clark C. (2016). Longitudinal associations between cyberbullying involvement and adolescent mental health. J. Adolesc. Health 59 502–509. 10.1016/j.jadohealth.2016.06.006 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Ferguson C. J., Munoz M. E., Garza A., Galindo M. (2014). Concurrent and prospective analyses of peer, television and social media influences on body dissatisfaction, eating disorder symptoms and life satisfaction in adolescent girls. J. Youth Adolesc. 43 1–14. 10.1007/s10964-012-9898-9 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Fisher B. W., Gardella J. H., Teurbe-Tolon A. R. (2016). Peer Cybervictimization among adolescents and the associated internalizing and externalizing problems: a meta-analysis. J. Youth Adolesc. 45 1727–1743. 10.1007/s10964-016-0541-z [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Foerster M., Roosli M. (2017). A latent class analysis on adolescents media use and associations with health related quality of life. Comput. Huma. Bahav. 71 266–274. 10.1016/j.chb.2017.02.015 [ CrossRef ] [ Google Scholar ]
  • Foody M., Samara M., O’Higgins Norman J. (2017). Bullying and cyberbullying studies in the school-aged population on the island of Ireland: a meta-analysis. Br. J. Educ. Psychol. 87 535–557. 10.1111/bjep.12163 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Fredrick S. S., Demaray M. K. (2018). Peer victimization and suicidal ideation: the role of gender and depression in a school. Based sample. J. Sch. Psychol. 67 1–15. 10.1016/j.jsp.2018.02.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Frison E., Eggermont S. (2016). Exploring the relationships between different types of Facebook use, perceived online social support, and adolescents’ depressed mood. Soc. Sci. Comput. Rev. 34 153–171. 10.1177/0894439314567449 [ CrossRef ] [ Google Scholar ]
  • Frison E., Eggermont S. (2017). Browsing, posting, and liking on instagram: the reciprocal relationships between different types of instagram use and adolescents’. Depressed Mood. 20 603–609. 10.1089/cyber.2017.0156 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Geusens F., Beullens K. (2017). The reciprocal associations between sharing alcohol references on social networking sites and binge drinking: a longitudinal study among late adolescents. Comput. Hum. Behav. 73 499–506. 10.1016/j.chb.2017.03.062 [ CrossRef ] [ Google Scholar ]
  • Geusens F., Beullens K. (2018). The association between social networking sites and alcohol abuse among Belgian adolescents: the role of attitudes and social norms. J. Media Psychol. 30 207–216. 10.1027/1864-1105/a000196 [ CrossRef ] [ Google Scholar ]
  • Griffiths M. D., Kuss D. J., Demetrovics Z. (2014). “ Chapter 6 - social networking addiction: an overview of preliminary findings ,” in Behavioral Addictions , eds Rosenberg K. P., Feder L. C. (San Diego: Academic Press), 119–141. [ Google Scholar ]
  • Hamm M. P., Newton A. S., Chisholm A., Shulhan J., Milne A., Sundar P., et al. (2015). Prevalence and effect of cyberbullying on children and young people: a scoping review of social media studies. JAMA Pediatr. 169 770–777. [ PubMed ] [ Google Scholar ]
  • Hanprathet N., Manwong M., Khumsri J., Yingyeun R., Phanasathit M. (2015). Facebook addiction and its relationship with mental health among thai high school students. J. Med. Assoc. Thailand 98(Suppl. 3) S81–S90. [ PubMed ] [ Google Scholar ]
  • Harbard E., Allen N. B., Trinder J., Bei B. (2016). What’s keeping teenagers up? prebedtime behaviors and actigraphy-assessed sleep over school and vacation. J. Adolesc. Health 58 426–432. 10.1016/j.jadohealth.2015.12.011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Hase C. N., Goldberg S. B., Smith D., Stuck A., Campain J. (2015). Impacts of traditional bullying and cyberbullying on the mental health of middle school and high school students. Psychol. Sch. 52 607–617. 10.1002/pits.21841 [ CrossRef ] [ Google Scholar ]
  • Holfeld B., Mishna F. (2019). , Internalizing symptoms and externalizing problems: risk factors for or consequences of cyber victimization? J. Youth Adolesc. 48 567–580. 10.1007/s10964-018-0974-7 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Houghton S., Lawrence D., Hunter S. C., Rosenberg M., Zadow C., Wood L., et al. (2018). Reciprocal relationships between trajectories of depressive symptoms and screen media use during adolescence. Youth Adolesc. 47 2453–2467. 10.1007/s10964-018-0901-y [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Jafarpour S., Jadidi H., Almadani S. A. H. (2017). Comparing personality traits, mental health and self-esteem in users and non-users of social networks. Razavi Int. J. Med. 5 :e61401. 10.5812/rijm.61401 [ CrossRef ] [ Google Scholar ]
  • John A., Glendenning A. C., Marchant A., Montgomery P., Stewart A., Wood S., et al. (2018). Self-harm, suicidal behaviours, and cyberbullying in children and young people: systematic review. J. Med. Int. Res. 20 : e129 . 10.2196/jmir.9044 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Kaplan A. M., Haenlein M. (2010). Users of the world, unite! The challenges and opportunities of social media. Bus. Horiz. 53 59–68. 10.1016/j.bushor.2009.09.003 [ CrossRef ] [ Google Scholar ]
  • Keles B., McCrae N., Grealish A. (2020). A systematic review: the influence of social media on depression, anxiety and psychological distress in adolescents. Int. J. Adolesc. Youth 25 79–93. 10.1080/02673843.2019.1590851 [ CrossRef ] [ Google Scholar ]
  • Kietzmann J. H., Hermkens K., McCarthy I. P., Silvestre B. S. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Bus. Horiz. 54 241–251. 10.1016/j.bushor.2011.01.005 [ CrossRef ] [ Google Scholar ]
  • Kim H. H.-S. (2017). The impact of online social networking on adolescent psychological well-being (WB): a population-level analysis of Korean school-aged children. Int. J. Adolesc. Youth 22 364–376. 10.1080/02673843.2016.1197135 [ CrossRef ] [ Google Scholar ]
  • Kim S., Kimber M., Boyle M. H., Georgiades K. (2019). Sex differences in the association between cyberbullying victimization and mental health. Subst. Suicid. Ideation Adolesc. 64 126–135. 10.1177/0706743718777397 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Kobayashi T., Boase J. (2012). No Such Effect? The implications of measurement error in self-report measures of mobile communication use. Commun. Methods Meas. 6 126–143. 10.1080/19312458.2012.679243 [ CrossRef ] [ Google Scholar ]
  • Koo H. J., Woo S., Yang E., Kwon J. H. (2015). The double meaning of online social space: three-way interactions among social anxiety, online social behavior, and offline social behavior. Cyberpsychol. Behav. Soc. Netw. 18 514–520. 10.1089/cyber.2014.0396 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Kraut R., Patterson M., Lundmark V., Kiesler S., Mukophadhyay T., Scherlis W. (1998). Internet paradox: a social technology that reduces social involvement and psychological well-being? Am. Psychol. 53 1017 . 10.1037/0003-066x.53.9.1017 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Krumpal I. (2013). Determinants of social desirability bias in sensitive surveys: a literature review. Qua. Quant. 47 2025–2047. 10.1007/s11135-011-9640-9 [ CrossRef ] [ Google Scholar ]
  • Lai H.-M., Hsieh P.-J., Zhang R.-C. (2018). Understanding adolescent students’ use of facebook and their subjective wellbeing: a gender-based comparison. Behav. Inform. Technol. 38 533–548. 10.1080/0144929x.2018.1543452 [ CrossRef ] [ Google Scholar ]
  • Lang D., Chien G. (2018). “wordcloud2”: a fast visualization tool for creating wordclouds by using “wordcloud2.js”. R Package Version 0.2.1. Available online at: https://cran.r-project.org/web/packages/wordcloud2/index.html [ Google Scholar ]
  • Larm P., Aslund C., Nilsson K. W. (2017). The role of online social network chatting for alcohol use in adolescence: testing three peer-related pathways in a Swedish population-based sample. Comput. Hum. Behav. 71 284–290. 10.1016/j.chb.2017.02.012 [ CrossRef ] [ Google Scholar ]
  • Larm P., Raninen J., Åslund C., Svensson J., Nilsson K. W. (2019). The increased trend of non-drinking alcohol among adolescents: what role do internet activities have? Eur. J. Public Health 29 27–32. 10.1093/eurpub/cky168 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Marchant A., Hawton K., Stewart A., Montgomery P., Singaravelu V., Lloyd K., et al. (2017). A systematic review of the relationship between internet use, self-harm and suicidal behaviour in young people: the good, the bad and the unknown. PLoS One 12 : e0181722 . 10.1371/journal.pone.0181722 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Marengo D., Longobardi C., Fabris M. A., Settanni M. (2018). Highly-visual social media and internalizing symptoms in adolescence: the mediating role of body image concerns. Comput. Hum. Behav. 82 63–69. 10.1016/j.chb.2018.01.003 [ CrossRef ] [ Google Scholar ]
  • Marques T. P., Marques-Pinto A., Alvarez M. J., Pereira C. R. (2018). Facebook: risks and opportunities in brazilian and portuguese youths with different levels of psychosocial adjustment. Spanish J. Psychol. 21 : E31 . [ PubMed ] [ Google Scholar ]
  • Mediakix (2019). 20 Tiktok Statistics Marketers Need To Know: Tiktok Demographics & Key Data. 2019. Available online at: https://mediakix.com/blog/top-tik-tok-statistics-demographics/ (accessed February 20, 2020). [ Google Scholar ]
  • Meier E. P., Gray J. (2014). Facebook photo activity associated with body image disturbance in adolescent girls. Cyberpsychol. Behav. Soc. Netw. 17 199–206. 10.1089/cyber.2013.0305 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Memon A. M., Sharma S. G., Mohite S. S., Jain S. (2018). The role of online social networking on deliberate self-harm and suicidality in adolescents: a systematized review of literature. Indian J. Psychiatry 60 384–392. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Merelle S. Y. M., Kleiboer A., Schotanus M., Cluitmans T. L. M., Waardenburg C. M., Kramer D., et al. (2017). Which health-related problems are associated with problematic video-gaming or social media use in adolescents? A large-scale cross-sectional study. Clin. Neuropsych. 14 11–19. [ Google Scholar ]
  • Mueller M. (2019). Challenging the Social Media Moral Panic: Preserving Free Expression under Hypertransparency. Washington, DC: Cato Institute Policy Analysis. [ Google Scholar ]
  • Naab T. K., Karnowski V., Schlütz D. (2019). Reporting mobile social media use: how survey and experience sampling measures differ. Commun. Methods Meas. 13 126–147. 10.1080/19312458.2018.1555799 [ CrossRef ] [ Google Scholar ]
  • Neira C. J., Barber B. L. (2014). Social networking site use: linked to adolescents’ social self-concept, self-esteem, and depressed mood. Austr. J. Psychol. 66 56–64. 10.1111/ajpy.12034 [ CrossRef ] [ Google Scholar ]
  • Nesi J., Miller A. B., Prinstein M. J. (2017a). Adolescents’ depressive symptoms and subsequent technology-based interpersonal behaviors: a multi-wave study. J. Appl. Dev. Psychol. 51 12–19. 10.1016/j.appdev.2017.02.002 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Nesi J., Rothenberg W. A., Hussong A. M., Jackson K. M. (2017b). Friends’ alcohol-related social networking site activity predicts escalations in adolescent drinking: mediation by peer norms. J. Adolesc. Health 60 641–647. 10.1016/j.jadohealth.2017.01.009 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Niu G. F., Luo Y. J., Sun X. J., Zhou Z. K., Yu F., Yang S. L., et al. (2018). Qzone use and depression among Chinese adolescents: a moderated mediation model. J. Affect. Disord. 231 58–62. 10.1016/j.jad.2018.01.013 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Nursalam N., Octavia M., Tristiana R. D., Efendi F. (2018). Association between insomnia and social network site use in Indonesian adolescents. Nurs. Forum 54 149–156. 10.1111/nuf.12308 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Oberst U., Wegmann E., Stodt B., Brand M., Chamarro A. (2017). Negative consequences from heavy social networking in adolescents: the mediating role of fear of missing out. J. Adolesc. 55 51–60. 10.1016/j.adolescence.2016.12.008 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • O’Connor R. C., Rasmussen S., Hawton K. (2014). Adolescent self-harm: a school-based study in Northern Ireland. J. Affect. Disord. 159 46–52. 10.1016/j.jad.2014.02.015 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Ofcom (2018). Children and Parents: Media Use and Attitudes Report. Warrington: Ofcom. [ Google Scholar ]
  • Olfson M., Druss B. G., Marcus S. C. (2015). Trends in mental health care among children and adolescents. N. Engl. J. Med. 372 2029–2038. 10.1056/nejmsa1413512 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Orben A. (2020). Teenagers, screens and social media: a narrative review of reviews and key studies. J. Soc. Psychiatry Psychiatr. Epidemiol. 55 407–414. 10.1007/s00127-019-01825-4 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Orben A., Dienlin T., Przybylski A. K. (2019). Social media’s enduring effect on adolescent life satisfaction. Pro. Natl. Acad. Sci. U.S.A. 116 10226–10228. 10.1073/pnas.1902058116 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Orben A., Przybylski A. K. (2019). The association between adolescent well-being and digital technology use. Nat. Hum. Behav. 3 173–182. 10.1038/s41562-018-0506-1 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • O’Reilly M., Dogra N., Whiteman N., Hughes J., Eruyar S., Reilly P. (2018). Is social media bad for mental health and wellbeing? Exploring the perspectives of adolescents. Clin. Chld Psychol. Psychiatry 23 601–613. 10.1177/1359104518775154 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Peters M., Godfrey C., McInerney P. (2017). “ Chapter 11: scoping reviews ,” in Joanna Briggs Institute Reviewer’s Manual , eds Aromataris E., Munn Z. (Adelaide: The Joanna Briggs Institute; ). [ Google Scholar ]
  • Peters M. D., Godfrey C., Khalil H., McInerney P., Parker D., Soares C. B. (2015). Guidance for conducting systematic scoping reviews. Int. J. Evi. -Based Healthc. 13 141–146. 10.1097/xeb.0000000000000050 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Przybylski A. K., Bowes L. (2017). Cyberbullying and adolescent well-being in England: a population-based cross-sectional study. Lancet Child Adolesc. Health 1 19–26. 10.1016/s2352-4642(17)30011-1 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Przybylski A. K., Weinstein N. (2017). A large-scale test of the goldilocks hypothesis. Psychol. Sci. 28 204–215. 10.1177/0956797616678438 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • R Core Team (2014). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. [ Google Scholar ]
  • Richards D., Caldwell P. H., Go H. (2015). Impact of social media on the health of children and young people. J. Paediatr. Child Health 51 1152–1157. 10.1111/jpc.13023 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Rousseau A., Eggermont S., Frison E. (2017). The reciprocal and indirect relationships between passive Facebook use, comparison on Facebook, and adolescents’ body dissatisfaction. Comput. Hum. Behav. 73 336–344. 10.1016/j.chb.2017.03.056 [ CrossRef ] [ Google Scholar ]
  • Salmela-Aro K., Upadyaya K., Hakkarainen K., Lonka K., Alho K. (2017). The dark side of internet use: two longitudinal studies of excessive internet use. depressive symptoms, school burnout and engagement among finnish early and late adolescents. J. Youth Adolesc. 46 343–357. 10.1007/s10964-016-0494-2 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Sampasa-Kanyinga H., Chaput J. P. (2016). Use of social networking sites and alcohol consumption among adolescents. Public Health 139 88–95. 10.1016/j.puhe.2016.05.005 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Sampasa-Kanyinga H., Hamilton H. A., Chaput J. P. (2018). Use of social media is associated with short sleep duration in a dose-response manner in students aged 11 to 20 years. Acta Paediatr. 107 694–700. 10.1111/apa.14210 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Sampasa-Kanyinga H., Lewis R. F. (2015). Frequent use of social networking sites is associated with poor psychological functioning among children and adolescents. Cyberpsychol. Behav. Soc. Netw. 18 380–385. 10.1089/cyber.2015.0055 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Scharkow M. (2016). The accuracy of self-reported internet Use—A validation study using client log data. Commun. Methods Meas. 10 13–27. 10.1080/19312458.2015.1118446 [ CrossRef ] [ Google Scholar ]
  • Scharkow M. (2019). The reliability and temporal stability of self-reported media exposure: a meta-analysis. Commun. Methods Meas. 13 198–211. 10.1080/19312458.2019.1594742 [ CrossRef ] [ Google Scholar ]
  • Schønning V., Aarø L. E., Skogen J. C. (2020). Central themes, core concepts and knowledge gaps concerning social media use, and mental health and well-being among adolescents: a protocol of a scoping review of published literature. BMJ Open 10 : e031105 . 10.1136/bmjopen-2019-031105 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Scott H., Woods H. C. (2018). Fear of missing out and sleep: cognitive behavioural factors in adolescents’ nighttime social media use. J. Adolesc. 68 61–65. 10.1016/j.adolescence.2018.07.009 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Seabrook E. M., Kern M. L., Rickard N. S. (2016). Social networking sites, depression, and anxiety: a systematic review. JMIR Ment. Health 3 : e50 . 10.2196/mental.5842 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Settanni M., Marengo D., Fabris M. A., Longobardi C. (2018). The interplay between ADHD symptoms and time perspective in addictive social media use: a study on adolescent Facebook users. Child. Youth Serv. Rev. 89 165–170. 10.1016/j.childyouth.2018.04.031 [ CrossRef ] [ Google Scholar ]
  • Silge J., Robinson D. (2016). tidytext: text mining and analysis using tidy data principles in RJ. Open Source Softw. 1 : 37 10.21105/joss.00037 [ CrossRef ] [ Google Scholar ]
  • Spears B. A., Taddeo C. M., Daly A. L., Stretton A., Karklins L. T. (2015). Cyberbullying, help-seeking and mental health in young Australians: implications for public health. Int. J. Public Health 60 219–226. 10.1007/s00038-014-0642-y [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Teesson M., Newton N. C., Slade T., Chapman C., Birrell L., Mewton L., et al. (2020). Combined prevention for substance use, depression, and anxiety in adolescence: a cluster-randomised controlled trial of a digital online intervention. Lancet Digital Health 2 e74–e84. 10.1016/s2589-7500(19)30213-4 [ CrossRef ] [ Google Scholar ]
  • Throuvala M. A., Griffiths M. D., Rennoldson M., Kuss D. J. (2019). Motivational processes and dysfunctional mechanisms of social media use among adolescents: a qualitative focus group study. Comput. Hum. Behav. 93 164–175. 10.1016/j.chb.2018.12.012 [ CrossRef ] [ Google Scholar ]
  • Tiggemann M., Slater A. (2017). Facebook and body image concern in adolescent girls: a prospective study. Int. J. Eat. Disord. 50 80–83. 10.1002/eat.22640 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Tricco A. C., Lillie E., Zarin W., O’Brien K. K., Colquhoun H., Levac D., et al. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann. Int. Med. 169 467–473. [ PubMed ] [ Google Scholar ]
  • Tseng F.-Y., Yang H.-J. (2015). Internet use and web communication networks, sources of social support, and forms of suicidal and nonsuicidal self-injury among adolescents: different patterns between genders. Suicide Life Threat. Behav. 45 178–191. 10.1111/sltb.12124 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Twenge J. M., Campbell W. K. (2019). Media use is linked to lower psychological well-being: evidence from three datasets. Psychiatr. Q. 11 311–331. 10.1007/s11126-019-09630-7 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Twenge J. M., Joiner T. E., Rogers M. L., Martin G. N. (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among U.S. adolescents after 2010 and links to increased new media screen time. Clin. Psychol. Sci. 6 3–17. 10.1177/2167702617723376 [ CrossRef ] [ Google Scholar ]
  • van den Eijnden R., Koning I., Doornwaard S., van Gurp F., ter Bogt T. (2018). The impact of heavy and disordered use of games and social media on adolescents’ psychological, social, and school functioning. J. Behav. Addict. 7 697–706. 10.1556/2006.7.2018.65 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Verduyn P., Ybarra O., Resibois M., Jonides J., Kross E. (2017). Do social network sites enhance or undermine subjective well-being? a critical review: do social network sites enhance or undermine subjective well-being?. Soc. Issues Policy Rev. 11 274–302. 10.1111/sipr.12033 [ CrossRef ] [ Google Scholar ]
  • Wallaroo (2020). TikTok Statistics – Updated February 2020. Available online at: https://wallaroomedia.com/blog/social-media/tiktok-statistics/ (accessed February 20, 2020). [ Google Scholar ]
  • Wang P., Wang X., Wu Y., Xie X., Wang X., Zhao F., et al. (2018). Social networking sites addiction and adolescent depression: A moderated mediation model of rumination and self-esteem. Personal. Individ. Differ. 127 162–167. 10.1016/j.paid.2018.02.008 [ CrossRef ] [ Google Scholar ]
  • Wartberg L., Kriston L., Thomasius R. (2018). Depressive symptoms in adolescents. Dtsch. Arztebl. Int. 115 549–555. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Weinstein E. (2018). The social media see-saw: positive and negative influences on adolescents’ affective well-being. New Media Soc. 20 3597–3623. 10.1177/1461444818755634 [ CrossRef ] [ Google Scholar ]
  • Wolke D., Lee K., Guy A. (2017). Cyberbullying: a storm in a teacup? Eur. Child Adolesc. Psychiatry 26 899–908. 10.1007/s00787-017-0954-6 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Woods H. C., Scott H. (2016). #Sleepyteens: social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem. J. Adolesc. 51 41–49. 10.1016/j.adolescence.2016.05.008 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Yan H., Zhang R., Oniffrey T. M., Chen G., Wang Y., Wu Y., et al. (2017). Associations among screen time and unhealthy behaviors. academic performance, and well-being in chinese adolescents. Int. J. Envion. Res. Public Heath. 14 : 596 . 10.3390/ijerph14060596 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

Depression Detection on Social Media: A Classification Framework and Research Challenges and Opportunities

  • Research Article
  • Published: 20 November 2023
  • Volume 8 , pages 88–120, ( 2024 )

Cite this article

  • Abdulrahman Aldkheel 1 &
  • Lina Zhou 2  

277 Accesses

Explore all metrics

Social media has become a safe space for discussing sensitive topics such as mental disorders. Depression dominates mental disorders globally, and accordingly, depression detection on social media has witnessed significant research advances. This study aims to review the current state-of-the-art research methods and propose a multidimensional framework to describe the current body of literature relating to detecting depression on social media. A study methodology involved selecting papers published between 2011 and 2023 that focused on detecting depression on social media. Five digital libraries were used to find relevant papers: Google Scholar, ACM digital library, PubMed, IEEE Xplore and ResearchGate. In selecting literature, two fundamental elements were considered: identifying papers focusing on depression detection and including papers involving social media use. In total, 50 papers were reviewed. Multiple dimensions were analyzed, including input features, social media platforms, disorder and symptomatology, ground truth, and techniques. Various types of input features were employed for depression detection, including textual, visual, behavioral, temporal, demographic, and spatial features. Among them, visual and spatial features have not been systematically reviewed to support mental health researchers in depression detection. Despite depression's fine-grained disorders, most studies focus on general depression. Recent studies have shown that social media data can be leveraged to identify depressive symptoms. Nevertheless, further research is needed to address issues like depression validation, generalizability, causes identification, and privacy and ethical considerations. An interdisciplinary collaboration between mental health professionals and computer scientists may help detect depression on social media more effectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

qualitative research on social media addiction

Data Availability

Not applicable .

Code Availability

Not applicable.

Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Press.

Marcus M, Yasamy MT, van van Ommeren M, Chisholm D, Saxena S (2012) Depression: A Global Public Health Concern: (517532013–004). Am Psychol Assoc. https://doi.org/10.1037/e517532013-004

Article   Google Scholar  

U.S. Department of Health and Human Services, National Institutes of Health, National Institute of Mental Health (2021) Depression (NIH Publication No. 21-MH-8079). U.S. Government Printing Office, Bethesda

Google Scholar  

The Australian Psychological Society (APS) Available: https://www.psychology.org.au/for-the-public/Psychology-topics/Depression . Accessed 08 Jun 2021

National Institute of Mental Health (2023) Depression. Available: https://www.nimh.nih.gov/health/topics/depression

Khalsa S-R, McCarthy KS, Sharpless BA, Barrett MS, Barber JP (2011) Beliefs about the causes of depression and treatment preferences. J Clin Psychol 67(6):539–549. https://doi.org/10.1002/jclp.20785

Salari N et al (2020) Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis. Global Health 16(1):57. https://doi.org/10.1186/s12992-020-00589-w

Li S, Wang Y, Xue J, Zhao N, Zhu T (2020) The impact of COVID-19 epidemic declaration on psychological consequences: a study on active Weibo users. IJERPH 17(6):2032. https://doi.org/10.3390/ijerph17062032

Halfin A (2007) Depression: the benefits of early and appropriate treatment. Am J Manag Care 13(4 Suppl):S92-97

Schomerus G, Angermeyer MC (2008) Stigma and its impact on help-seeking for mental disorders: what do we know? Epidemiol Psichiatr Soc 17(1):31–37. https://doi.org/10.1017/S1121189X00002669

Dey S, Sarkar I, Chakraborty S, Roy S (2020) Depression detection using intelligent algorithms from social media context - state of the art, trends and future roadmap. jxu 14(8). https://doi.org/10.37896/jxu14.8/007

Guntuku SC, Yaden DB, Kern ML, Ungar LH, Eichstaedt JC (2017) Detecting depression and mental illness on social media: an integrative review. Curr Opin Behav Sci 18:43–49. https://doi.org/10.1016/j.cobeha.2017.07.005

Murrieta J, Frye CC, Sun L, Ly LG, Cochancela CS, Eikey EV (2018) #Depression: findings from a literature review of 10 years of social media and depression research. In: Chowdhury G, McLeod J, Gillet V, Willett P (eds) Transforming Digital Worlds, vol 10766. Springer International Publishing, Cham, pp 47–56. https://doi.org/10.1007/978-3-319-78105-1_6 (Lecture Notes in Computer Science)

Chapter   Google Scholar  

Salas-Zárate R, Alor-Hernández G, Salas-Zárate M del P, Paredes-Valverde MA, Bustos-López M, Sánchez-Cervantes JL (2022) Detecting depression signs on social media: a systematic literature review. Healthcare 10(2):291. https://doi.org/10.3390/healthcare10020291

Zafar A, Chitnis S (2020) Survey of Depression Detection using Social Networking Sites via Data Mining. In: 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India: IEEE, pp 88–93. https://doi.org/10.1109/Confluence47617.2020.9058189

Yazdavar AH et al (2020) Multimodal mental health analysis in social media. PLoS ONE 15(4):e0226248. https://doi.org/10.1371/journal.pone.0226248

Willers J (2017) Methods for extracting data from the Internet, Master of Science, Iowa State University, Digital Repository, Ames. https://doi.org/10.31274/etd-180810-5256

Sekulić I, Gjurković M, Šnajder J (2019) Not Just Depressed: Bipolar Disorder Prediction on Reddit. arXiv. [Online]. Available: http://arxiv.org/abs/1811.04655 . Accessed 27 Sep 2022

Chen X, Sykora M, Jackson T, Elayan S, Munir F (2018) Tweeting your mental health: an exploration of different classifiers and features with emotional signals in identifying mental health conditions, presented at the Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2018.421 .

Cavazos-Rehg PA et al (2016) A content analysis of depression-related tweets. Comput Hum Behav 54:351–357. https://doi.org/10.1016/j.chb.2015.08.023

De Choudhury M, Gamon M, Counts S, Horvitz E (2013) Predicting Depression via Social Media AAAI. [Online] Available: https://www.microsoft.com/en-us/research/publication/predicting-depression-via-social-media/ . Accessed 17 Nov 2020

Moreno MA et al (2011) Feeling bad on Facebook: depression disclosures by college students on a social networking site. Depress Anxiety 28(6):447–455. https://doi.org/10.1002/da.20805

Nadeem M (2016) Identifying Depression on Twitter, arXiv:1607.07384 [cs, stat] . [Online]. Available: http://arxiv.org/abs/1607.07384 . Accessed 08 Jul 2021

Trifan A, Semeraro D, Drake J, Bukowski R, Oliveira JL (2020) Social media mining for postpartum depression prediction. Stud Health Technol Inform 270:1391–1392. https://doi.org/10.3233/SHTI200457

De Choudhury M, Counts S, Horvitz EJ, Hoff A (2014) Characterizing and predicting postpartum depression from shared facebook data. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing. Baltimore Maryland USA: ACM, pp 626–638. https://doi.org/10.1145/2531602.2531675

De Choudhury M, Kiciman E, Dredze M, Coppersmith G, Kumar M (2016) Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. San Jose California USA: ACM, pp 2098–2110. https://doi.org/10.1145/2858036.2858207

Cavazos-Rehg PA et al (2017) An analysis of depression, self-harm, and suicidal ideation content on Tumblr. Crisis 38(1):44–52. https://doi.org/10.1027/0227-5910/a000409

Tefera NL, Zhou L (2018) A Scorecard Method for Detecting Depression in Social Media Users, presented at the Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2018.071

Schwartz HA et al (2014) Towards Assessing Changes in Degree of Depression through Facebook. In: Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. Baltimore, Maryland, USA: Association for Computational Linguistics, pp 118–125. https://doi.org/10.3115/v1/W14-3214

Mustafa RU, Ashraf N, Ahmed FS, Ferzund J, Shahzad B, Gelbukh A (2020) A Multiclass Depression Detection in Social Media Based on Sentiment Analysis. In: Latifi S (ed) 17th International Conference on Information Technology–New Generations (ITNG 2020), in Advances in Intelligent Systems and Computing, vol. 1134. Cham: Springer International Publishing, pp 659–662. https://doi.org/10.1007/978-3-030-43020-7_89

Hootsuite & We Are Social (2021) Digital 2021 Global Digital Overview. Available: https://datareportal.com/reports/digital-2021-global-overview-report . Accessed 02 Jun 2021

Park S, Kim I, Lee SW, Yoo J, Jeong B, Cha M (2015) Manifestation of Depression and Loneliness on Social Networks: A Case Study of Young Adults on Facebook. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing. Vancouver BC Canada: ACM, pp 557–570. https://doi.org/10.1145/2675133.2675139

Reece AG, Danforth CM (2016) Instagram photos reveal predictive markers of depression, arXiv:1608.03282 [physics] . [Online]. Available: http://arxiv.org/abs/1608.03282 . Accessed 07 Jul 2021

Lup K, Trub L, Rosenthal L (2015) Instagram #Instasad?: exploring associations among instagram use, depressive symptoms, negative social comparison, and strangers followed. Cyberpsychol Behav Soc Netw 18(5):247–252. https://doi.org/10.1089/cyber.2014.0560

De Choudhury M, Counts S, Horvitz E (2013) Social media as a measurement tool of depression in populations. In: Proceedings of the 5th Annual ACM Web Science Conference on - WebSci ’13. Paris, France: ACM Press, pp 47–56. https://doi.org/10.1145/2464464.2464480

Wongkoblap A, Vadillo MA, Curcin V (2018) A Multilevel Predictive Model for Detecting Social Network Users with Depression. In: 2018 IEEE International Conference on Healthcare Informatics (ICHI), New York, NY: IEEE, pp 130–135. https://doi.org/10.1109/ICHI.2018.00022

Ricard BJ, Marsch LA, Crosier B, Hassanpour S (2018) Exploring the utility of community-generated social media content for detecting depression: an analytical study on Instagram. J Med Internet Res 20(12):e11817. https://doi.org/10.2196/11817

Park M, Cha C, Cha M (2012) Depressive Moods of Users Portrayed in Twitter. In: Proceedings of the ACM SIGKDD Workshop on healthcare informatics (HI-KDD)

Reece AG, Reagan AJ, Lix KLM, Dodds PS, Danforth CM, Langer EJ (2017) Forecasting the onset and course of mental illness with Twitter data. Sci Rep 7(1):13006. https://doi.org/10.1038/s41598-017-12961-9

Carey JL et al (2018) SoMe and Self Harm: The Use of Social Media in Depressed and Suicidal Youth, presented at the Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2018.420

Guntuku SC, Preotiuc-Pietro D, Eichstaedt JC, Ungar LH (2019) What Twitter Profile and Posted Images Reveal About Depression and Anxiety, arXiv:1904.02670 [cs] . [Online]. Available: http://arxiv.org/abs/1904.02670 . Accessed 09 Jul 2021

Kroenke K, Spitzer RL, Williams JBW (2001) The PHQ-9: Validity of a brief depression severity measure. J Gen Intern Med 16(9):606–613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x

Radloff LS (1977) The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas 1(3):385–401. https://doi.org/10.1177/014662167700100306

Beck A, Steer R (1988) Manual for the Beck Hopelessness Scale. The Psychological Corporation

Goldberg L (1999) A broad-bandwidth, public domain, personality inventory measuring the lower level facets of several five-factor models. Tilburg Univ. Press, Tilburg

Andalibi N, Ozturk P, Forte A (2015) Depression-related Imagery on Instagram. In: Proceedings of the 18th ACM Conference Companion on Computer Supported Cooperative Work & Social Computing. Vancouver BC Canada: ACM, pp 231–234. https://doi.org/10.1145/2685553.2699014

Armin N (2021) Understanding depression during the COVID-19 pandemic through social media data. The University of Mississippi

Chen X, Sykora MD, Jackson TW, Elayan S (2018) What about Mood Swings: Identifying Depression on Twitter with Temporal Measures of Emotions. In: Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW ’18. Lyon, France: ACM Press, pp 1653–1660. https://doi.org/10.1145/3184558.3191624

Husseini Orabi A, Buddhitha P, Husseini Orabi M, Inkpen D (2018) Deep Learning for Depression Detection of Twitter Users. In: Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic. New Orleans, LA: Association for Computational Linguistics, pp 88–97. https://doi.org/10.18653/v1/W18-0609

Islam MR, Kamal ARM, Sultana N, Islam R, Moni MA, ulhaq A (2018) Detecting Depression Using K-Nearest Neighbors (KNN) Classification Technique. In: 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2). Rajshahi: IEEE, pp 1–4. https://doi.org/10.1109/IC4ME2.2018.8465641

Islam MdR, Kabir MA, Ahmed A, Kamal ARM, Wang H, Ulhaq A (2018) Depression detection from social network data using machine learning techniques. Health Inf Sci Syst 6(1):8. https://doi.org/10.1007/s13755-018-0046-0

Jamil Z, Inkpen D, Buddhitha P, White K (2017) Monitoring Tweets for Depression to Detect At-risk Users. In: Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology –- From Linguistic Signal to Clinical Reality. Vancouver, BC: Association for Computational Linguistics, pp 32–40. https://doi.org/10.18653/v1/W17-3104

Lachmar EM, Wittenborn AK, Bogen KW, McCauley HL (2017) #MyDepressionLooksLike: examining public discourse about depression on Twitter. JMIR Ment Health 4(4):e43. https://doi.org/10.2196/mental.8141

Husain M (2019) Social media analytics to predict depression level in the users. https://doi.org/10.4018/978-1-5225-8567-1

Pirina I, Çöltekin Ç (2018) Identifying Depression on Reddit: The Effect of Training Data. In: Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task. Brussels, Belgium: Association for Computational Linguistics, pp 9–12. https://doi.org/10.18653/v1/W18-5903

Resnik P, Armstrong W, Claudino L, Nguyen T, Nguyen V-A, Boyd-Graber J (2015) Beyond LDA: Exploring Supervised Topic Modeling for Depression-Related Language in Twitter. In: Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. Denver, Colorado: Association for Computational Linguistics, pp 99–107. https://doi.org/10.3115/v1/W15-1212

Sadeque F, Xu D, Bethard S (2018) Measuring the Latency of Depression Detection in Social Media. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. Marina Del Rey CA USA: ACM, pp 495–503. https://doi.org/10.1145/3159652.3159725

Shen G et al (2017) Depression Detection via Harvesting Social Media: A Multimodal Dictionary Learning Solution. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. Melbourne, Australia: International Joint Conferences on Artificial Intelligence Organization, pp 3838–3844. https://doi.org/10.24963/ijcai.2017/536

Stankevich M, Isakov V, Devyatkin D, Smirnov I (2018) Feature Engineering for Depression Detection in Social Media. In: Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods. Funchal, Madeira, Portugal: SCITEPRESS - Science and Technology Publications, pp 426–431. https://doi.org/10.5220/0006598604260431

Tadesse MM, Lin H, Xu B, Yang L (2019) Detection of depression-related posts in Reddit social media forum. IEEE Access 7:44883–44893. https://doi.org/10.1109/ACCESS.2019.2909180

Maupomés D, Meurs M (2018) Using topic extraction on social media content for the early detection of depression in CLEF (Working Notes). Available: https://CEUR-WS.org . Accessed 26 Nov 2020

Alsagri HS, Ykhlef M (2020) Machine learning-based approach for depression detection in Twitter using content and activity features. IEICE Trans Inf Syst E103.D(8):1825–1832. https://doi.org/10.1587/transinf.2020EDP7023

Zhou J, Zogan H, Yang S, Jameel S, Xu G, Chen F (2020) Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia. arXiv:2007.02325 [cs] . [Online]. Available: http://arxiv.org/abs/2007.02325 . Accessed 09 Jul 2021

Zogan H, Razzak I, Wang X, Jameel S, Xu G (2022) Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media. World Wide Web 25(1):281–304. https://doi.org/10.1007/s11280-021-00992-2

Hassan AU, Hussain J, Hussain M, Sadiq M, Lee S (2017) Sentiment analysis of social networking sites (SNS) data using machine learning approach for the measurement of depression. In: 2017 International Conference on Information and Communication Technology Convergence (ICTC), Jeju: IEEE, pp 138–140. https://doi.org/10.1109/ICTC.2017.8190959 .

Chiong R, Budhi GS, Dhakal S, Chiong F (2021) A textual-based featuring approach for depression detection using machine learning classifiers and social media texts. Comput Biol Med 135:104499. https://doi.org/10.1016/j.compbiomed.2021.104499

Fast E, Chen B, Bernstein M (2016) Empath: Understanding Topic Signals in Large-Scale Text. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp 4647–4657. https://doi.org/10.1145/2858036.2858535

Lin C et al (2020) SenseMood: Depression Detection on Social Media. In: Proceedings of the 2020 International Conference on Multimedia Retrieval. Dublin Ireland: ACM, pp 407–411. https://doi.org/10.1145/3372278.3391932

Andalibi N, Ozturk P, Forte A (2017) Sensitive Self-disclosures, Responses, and Social Support on Instagram: The Case of #Depression. In: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. Portland Oregon USA: ACM, pp 1485–1500. https://doi.org/10.1145/2998181.2998243

Nielsen FÅ (2011) A new ANEW: Evaluation of a word list for sentiment analysis in microblogs, arXiv:1103.2903 [cs] . [Online]. Available: http://arxiv.org/abs/1103.2903 . Accessed 09 Jul 2021

Bigun J (2006) Vision with Direction: a Systematic Introduction to Image Processing and Computer Vision. Springer-Verlag Berlin Heidelberg, Berlin, Heidelberg

Lustberg L, Reynolds CF (2000) Depression and insomnia: questions of cause and effect. Sleep Med Rev 4(3):253–262. https://doi.org/10.1053/smrv.1999.0075

Kim J, Kim H (2017) Demographic and environmental factors associated with mental health: a cross-sectional study. IJERPH 14(4):431. https://doi.org/10.3390/ijerph14040431

Article   MathSciNet   Google Scholar  

Cash S, Schwab-Reese LM, Zipfel E, Wilt M, Moreno M (2020) What college students post about depression on Facebook and the support they perceive: content analysis. JMIR Form Res 4(7):e13650. https://doi.org/10.2196/13650

Gui T, Zhang Q, Zhu L, Zhou X, Peng M, Huang X (2019) Depression detection on social media with reinforcement learning. In: China National Conference on Chinese Computational Linguistics. Springer, pp 613–624

Zogan H, Razzak I, Jameel S, Xu G (2023) Hierarchical convolutional attention network for depression detection on social media and its impact during pandemic. IEEE J Biomed Health Inform: 1–9. https://doi.org/10.1109/JBHI.2023.3243249

Cui B, Wang J, Lin H, Zhang Y, Yang L, Xu B (2022) Emotion-based reinforcement attention network for depression detection on social media: algorithm development and validation. JMIR Med Inform 10(8):e37818. https://doi.org/10.2196/37818

Eye BB (2020) Depression Analysis. Kaggle

Padrez KA et al (2016) Linking social media and medical record data: a study of adults presenting to an academic, urban emergency department. BMJ Qual Saf 25(6):414–423. https://doi.org/10.1136/bmjqs-2015-004489

Srinivasan J, Cohen NL, Parikh SV (2003) Patient attitudes regarding causes of depression: implications for psychoeducation. Can J Psychiatry 48(7):493–495. https://doi.org/10.1177/070674370304800711

Hansson M, Chotai J, Bodlund O (2010) Patients’ beliefs about the cause of their depression. J Affect Disord 124(1–2):54–59. https://doi.org/10.1016/j.jad.2009.10.032

Addis ME, Truax P, Jacobson NS (1995) Why do people think they are depressed?: The reasons for depression questionnaire. Psychotherapy: Theory Res Pract Train 32(3):476–483. https://doi.org/10.1037/0033-3204.32.3.476

Inkster B, Stillwell D, Kosinski M, Jones P (2016) A decade into Facebook: where is psychiatry in the digital age? Lancet Psychiatry 3(11):1087–1090. https://doi.org/10.1016/S2215-0366(16)30041-4

Chentsova-Dutton YE, Tsai JL (2009) Understanding depression across cultures. In: Gotlib IH, Hammen CL (eds) Handbook of depression, 2nd edn. Guilford Press, pp 363–385

Loveys K, Torrez J, Fine A, Moriarty G, Coppersmith G (2018) Cross-cultural differences in language markers of depression online. In: Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic. New Orleans, LA: Association for Computational Linguistics, pp 78–87. https://doi.org/10.18653/v1/W18-0608

Tsugawa S, Kikuchi Y, Kishino F, Nakajima K, Itoh Y, Ohsaki H (2015) Recognizing Depression from Twitter Activity. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. Seoul Republic of Korea: ACM, pp 3187–3196. https://doi.org/10.1145/2702123.2702280

Jain SH, Powers BW, Hawkins JB, Brownstein JS (2015) The digital phenotype. Nat Biotechnol 33(5):462–463. https://doi.org/10.1038/nbt.3223

Sanyal H, Shukla S, Agrawal R (2021) Study of Depression Detection using Deep Learning. In: 2021 IEEE International Conference on Consumer Electronics (ICCE). Las Vegas, NV, USA: IEEE, pp 1–5. https://doi.org/10.1109/ICCE50685.2021.9427624

Miner AS, Milstein A, Schueller S, Hegde R, Mangurian C, Linos E (2016) Smartphone-based conversational agents and responses to questions about mental health, interpersonal violence, and physical health. JAMA Intern Med 176(5):619. https://doi.org/10.1001/jamainternmed.2016.0400

Statista (2021) Number of smartphone users in the United States from 2009 to 2040. Available: https://www.statista.com/statistics/201182/forecast-of-smartphone-users-in-the-us/ . Accessed 21 Jan 2021

Del Valle K (2018) Conversational commerce: A new opportunity for card payments. MasterCard. Available: https://dokumen.tips/documents/conversational-commerce-a-new-opportunity-for-conversational-commerce-a-new.html?page=1 . Accessed 11 Nov 2023

Birmaher B et al (1996) Childhood and adolescent depression: a review of the past 10 years. Part I. J Am Acad Child Adolesc Psychiatry 35(11):1427–1439. https://doi.org/10.1097/00004583-199611000-00011

Judd L, Paulus M, Wells K, Rapaport M (1996) Socioeconomic burden of subsyndromal depressive symptoms and major depression in a sample of the general population. AJP 153(11):1411–1417. https://doi.org/10.1176/ajp.153.11.1411

Weissman MM (1999) Depressed adolescents grown up. JAMA 281(18):1707. https://doi.org/10.1001/jama.281.18.1707

National Collaborating Centre for Mental Health (Great Britain), Ed., Depression in adults with a chronic physical health problem: treatment and management: National clinical practice guideline 91 . London: British Psychological Society and the Royal College of Psychiatrists, 2010

Hao F, Pang G, Wu Y, Pi Z, Xia L, Min G (2019) Providing appropriate social support to prevention of depression for highly anxious sufferers. IEEE Trans Comput Soc Syst 6(5):879–887. https://doi.org/10.1109/TCSS.2019.2894144

Davies EB, Morriss R, Glazebrook C (2014) Computer-delivered and web-based interventions to improve depression, anxiety, and psychological well-being of university students: a systematic review and meta-analysis. J Med Internet Res 16(5):e130. https://doi.org/10.2196/jmir.3142

Lattie EG, Adkins EC, Winquist N, Stiles-Shields C, Wafford QE, Graham AK (2019) Digital mental health interventions for depression, anxiety, and enhancement of psychological well-being among college students: systematic review. J Med Internet Res 21(7):e12869. https://doi.org/10.2196/12869

Download references

This research received no external funding.

Author information

Authors and affiliations.

Department of Software and Information Systems, The University of North Carolina at Charlotte, Charlotte, NC, USA

Abdulrahman Aldkheel

Department of Business Information Systems and Operations Management, The University of North Carolina at Charlotte, Charlotte, NC, USA

You can also search for this author in PubMed   Google Scholar

Contributions

A.A. designed and conducted the survey, analyzed the data, designed the framework, and wrote the initial draft of the manuscript. L.Z. provided critical feedback on the manuscript, contributed to the data interpretation, drafted, revised, and approved the manuscript. All authors reviewed and approved the final version of the manuscript.

Corresponding author

Correspondence to Abdulrahman Aldkheel .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Aldkheel, A., Zhou, L. Depression Detection on Social Media: A Classification Framework and Research Challenges and Opportunities. J Healthc Inform Res 8 , 88–120 (2024). https://doi.org/10.1007/s41666-023-00152-3

Download citation

Received : 06 April 2023

Revised : 24 October 2023

Accepted : 06 November 2023

Published : 20 November 2023

Issue Date : March 2024

DOI : https://doi.org/10.1007/s41666-023-00152-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Social media
  • Depression detection
  • Mental health
  • Machine Learning
  • Find a journal
  • Publish with us
  • Track your research

IMAGES

  1. (PDF) A Qualitative Study on the Reasons for Social Media Addiction

    qualitative research on social media addiction

  2. (DOC) Social Media Addiction and its Effects to Senior High School

    qualitative research on social media addiction

  3. Social Media Addiction: 11 Signs, Causes, Tips To Break It

    qualitative research on social media addiction

  4. 50 Shocking Social Media Addiction Statistics: 2024 Ultimate Guide

    qualitative research on social media addiction

  5. (PDF) Why people are becoming addicted to social media: A qualitative study

    qualitative research on social media addiction

  6. The Real Social Media Addiction Stats for 2021

    qualitative research on social media addiction

VIDEO

  1. The Truth About Social Media #health #shorts

  2. Social Media Addiction 📲, Get Ultimate Time for Study 🧠 Do this Only 🙏by #abhisheksir #geniusbatch

  3. social media addiction

  4. Social Media Addiction

  5. Exposing : The Dark Truth behind Social Media

  6. How Social Media Manipulates Us ! Piscium

COMMENTS

  1. Why people are becoming addicted to social media: A qualitative study

    Social media addiction (SMA) led to the formation of health-threatening behaviors that can have a negative impact on the quality of life and well-being. Many factors can develop an exaggerated tendency to use social media (SM), which can be prevented in most cases. ... This study is a qualitative research which builds on conventional content ...

  2. Research trends in social media addiction and problematic social media

    In terms of bibliometric analysis of social media addiction research, few studies have attempted to review the existing literature in the domain extensively. ... Thus, future qualitative research to assess engagement based ranking frameworks is extremely necessary in order to provide a broader perspective on social media use and tackle key ...

  3. Why people are becoming addicted to social media: A qualitative study

    Social media addiction (SMA) was assessed using the Bergen Social Media Addiction Scale. Results: The results revealed that 88.5% (n = 1593) of the participants were SM users, and the average time ...

  4. (PDF) SOCIAL MEDIA ADDICTION AND YOUNG PEOPLE: A ...

    Results: The need of satisfaction in real-life relationship among young people become the most common factors of addiction, addiction to social media will caused mental health problem among them ...

  5. Social Media Use and Its Connection to Mental Health: A Systematic

    The structure of social media influences on mental health needs to be further analyzed through qualitative research and vertical cohort studies. Keywords: social media, mental health, systematic review, prisma. Introduction and background. ... activity, and addiction to social media. In today's world, anxiety is one of the basic mental health ...

  6. PDF Qualitative Research on Social Media Addictions of Psychological

    Psychological counselor, social media addiction, qualitative research, focus group interview 1. Introduction Depending on the intensive use of the Internet in our lives, communication technology tools have developed, and one of these tools, social media, has started to be used more frequently. Social media is defined as an

  7. PDF Qualitative Research on Youths' Social Media Use: A review of the

    Schmeichel, Mardi; Hughes, Hilary E.; and Kutner, Mel (2018) "Qualitative Research on Youths' Social Media Use: A review of the literature," Middle Grades Review: Vol. 4 : Iss. 2 , Article 4. This Research is brought to you for free and open access by the College of Education and Social Services at ScholarWorks @ UVM.

  8. Exploring adolescents' perspectives on social media and mental health

    Reduced social media use has also been correlated with improved psychological outcomes (Hunt et al., 2018).Best et al.'s. (2014) systematic review evaluated quantitative and qualitative data on the effects of social media on adolescent wellbeing. Its benefits included social support, self-expression and access to online mental health resources, but significant negative aspects included ...

  9. A Qualitative Study on the Reasons for Social Media Addiction

    The findings of the research showed that participants' reasons for using social media were lack of friends, social necessity of social media, feeling of fulfillment, fear of missing out, intertwining of social media and daily life. The study also pointed out that social media addiction has a beginning and a continuity phase.

  10. Qualitative and Mixed Methods Social Media Research: A Review of the

    Kaplan and Haenlein (2010) defined social media as "… a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of User Generated Content" (p. 61). The emergence of social media technologies has been embraced by a growing number of users who post text messages, pictures, and videos online ...

  11. Smartphone and social media addiction: Exploring the perceptions and

    "Addiction" to internet-connected technology continues to dominate media discourses of young people. Researchers have identified negative outcomes, including decreased mental health, resulting from anxieties related to technology, e.g., a fear of missing out and social connectivity related to online technologies.

  12. The relationship between post-traumatic stress disorder and social

    The relationship between post-traumatic stress disorder and social media addiction: A qualitative study. Author links open overlay panel Anthony Thomas Fantasia, Gayle Prybutok ... aspect of the current conversation about this pervasive problem is understanding the cognitive mechanisms behind social media addiction. The research done by Basu ...

  13. Why people are becoming addicted to social media: A qualitative study

    Abstract. Background: Social media addiction (SMA) led to the formation of health-threatening behaviors that can have a negative impact on the quality of life and well-being. Many factors can develop an exaggerated tendency to use social media (SM), which can be prevented in most cases. This study aimed to explore the reasons for SMA.

  14. Methodology: Toward an Understanding of Social Media Addiction

    Abstract. This chapter explains in detail the methodology process to study explanations for the social media addiction phenomenon in Generation Z users as well as the underlying reasons for such behavior. For this purpose, a qualitative method was used to obtain deep insights about the topic. First, participants completed the BSMAS to ensure ...

  15. A qualitative study on negative experiences of social media use and

    Introduction. The marked rise in social media use today exemplifies the evolution of the digital landscape. Social media platforms such as Facebook and YouTube continue to dominate the online scene with an estimated 2.9 and 2.1 billion monthly active users respectively [1, 2].Other platforms such as Instagram and TikTok, developed later, have since gained traction, especially among younger ...

  16. A Qualitative Study on the Reasons for Social Media Addiction

    The aim of this study was to determine the causes of social media addiction of individuals, who define themselves as social media addicts, in a clearer and more concrete way. In order to achieve this aim, participants have been tested with an addiction test, and 25 university students who perceive themselves as social media addicts were selected for the study. The findings of the research ...

  17. A Qualitative Study on the Reasons for Social Media Addiction

    This qualitative research article aims to explore the phenomenon of social media addiction among university students in Malaysia, examining the underlying factors contributing to addictive behaviours.

  18. Social Media Addiction in High School Students: A Cross ...

    2.1 Study Design. This is a cross-sectional, correlational type of research. In this study, which was conducted in order to determine the relationship of social media addiction with sleep quality and psychological problems in high school students, a path analysis study was made in line with the examined literature and the aim, and the theoretical model is shown in Fig. 1.

  19. Frontiers

    Five of the 10 most productive journals in the field of social media addiction research are published by Elsevier (all Q1 rankings) while Springer and Frontiers Media published one journal each. ... Thus, future qualitative research to assess engagement based ranking frameworks is extremely necessary in order to provide a broader perspective on ...

  20. Social media in qualitative research: Challenges and ...

    The challenges of using social media in qualitative research are many. These challenges are related to the large volume of data, the nature of digital texts, visual cues, and types of behaviour on social media sites, the authenticity of the data, the level of access obtained, and the digital divide in some situations.

  21. Qualitative Research on Social Media Addictions of Psychological

    Social media (SM), which is frequently preferred by young people, poses a risk of addiction when used excessively and unconsciously. Having self-awareness and becoming digitally literate, unlike ...

  22. Social Media Use and Mental Health and Well-Being Among Adolescents

    Introduction: Social media has become an integrated part of daily life, with an estimated 3 billion social media users worldwide. Adolescents and young adults are the most active users of social media. Research on social media has grown rapidly, with the potential association of social media use and mental health and well-being becoming a polarized and much-studied subject.

  23. Depression Detection on Social Media: A Classification ...

    Social media has become a safe space for discussing sensitive topics such as mental disorders. Depression dominates mental disorders globally, and accordingly, depression detection on social media has witnessed significant research advances. This study aims to review the current state-of-the-art research methods and propose a multidimensional framework to describe the current body of ...

  24. PDF A Qualitative Study on the Reasons for Social Media Addiction

    862 AKSOY / A Qualitative Study on the Reasons for Social Media Addiction Bridgestock (2016) compared the students' reasons for using Facebook according to the continent they lived in. The most ...