speech about covid 19 pandemic for students in philippines

Philippines

The Philippines Still Hasn’t Fully Reopened Its Schools Because of COVID-19. What Is This Doing to Children?

Remote Learning Aims To Bridge Philippines Pandemic Gaps

I f 17-year-old Ruzel Delaroso needs to ask her teacher a question, she can’t simply raise her hand, much less fire off an email from the kitchen table. She has to leave the modest shack that her family calls home in Januiay, a farming town in the central Philippines, and head to an area of dense shrubbery, a 10-minute walk away. There, if she’s lucky, she can pick up a phone signal and finally ask about the math problem in the self-learning materials her mother picked up from school.

“We’re so used to our teachers always being around,” Delaroso tells TIME via the same temperamental phone connection. “But now it’s harder to communicate with them.”

Her school, Calmay National High School, is among the tens of thousands of Philippine public schools shuttered since March 2020 because of the COVID-19 pandemic, and Delaroso is one of 1.6 billion children affected by worldwide school closures, according to a UNESCO estimate.

But while other countries have taken the opportunity to resume in-person classes, the Philippines has lagged behind. After 20 months of pandemic prevention measures, amounting to one of the world’s longest lockdowns , only 5,000 students, in just over 100 public schools, have been allowed to go back to class in a two-month trial program—a tiny fraction of the 27 million public school students who enrolled this year. The Philippines must be one of a very few countries, if not the only country, to remain so reliant on distance learning. It has become a vast experiment in life without in-person schooling.

Read More: What It’s Like Being a Teacher During the COVID-19 Pandemic

“[Education secretary Leonor Briones] always reminds us that in the past when there were military sieges, or volcanic eruptions, earthquakes, typhoons, floods, learning continued,” says education undersecretary Diosdado San Antonio.

But has it this time? Educators fear that prolonged closure is having negative effects on students’ ability to learn, impacting their futures just a time when the country needs a young, well-educated workforce to resume the impressive economic growth it was enjoying before the pandemic hit.

Globally, COVID-19 will be impacting the mental health of children and young adolescents for years to come, UNICEF warns. School shutdowns have already been blamed for a rise in dropout rates and decreased literacy, and the World Bank estimates that the number of children aged 10 and below, from low- and middle-income countries, who cannot read simple text has risen from 53% prior to the pandemic to 70% today.

If the pilot resumption of classes passes without incident, there are hopes for a wider reopening of Philippine schools. But without it, there are fears of a lost generation .

How COVID-19 impacted Philippine education

From March 2020 to September 2021, UNICEF tallied 131 million pre-tertiary students from 11 countries who had been trying to learn at home for at least three quarters of the time that they would normally have been in school. Of that number, 66 million came from just two countries where face-to-face classes were almost completely nixed: Bangladesh and the Philippines. (Bangladesh reopened its schools in September.)

Amid the initial COVID-19 surge of March 2020—just weeks shy of the end of the academic year—the Philippines stopped in-person classes for its entire cohort of public education students, which then numbered some 24.9 million according to UNESCO. The start of the new school year in September also got pushed back, as President Rodrigo Duterte imposed a “no vaccine, no classes” policy.

When schooling finally resumed in October 2020, the education department’s solution was a blend of remote-learning options: online platforms, educational TV and radio, and printed modules. But social inequalities and the lack of resources at home to support these approaches have dealt a huge blow to many students and teachers.

A departmental report released in March 2021 found that 99% of public school students got passing marks for the first academic quarter of last year. But other surveys claim that students are being disadvantaged. Over 86% of the 1,299 students polled by the Movement for Safe, Equitable, Quality and Relevant Education said they learned less through the education department’s take-home modules—so did 66% of those using online learning and 74% using a blend of online learning and hard-copy material.

Read More: Angelina Jolie on Why We Can Let COVID-19 Derail Education

Even though she’s an academic topnotcher—getting a weighted grade average of 91 out of 100 last year—Delaroso also feels that remote learning is inferior.

At Delaroso’s high school, teacher Johnnalie Consumo, 25, has detected a lack of eagerness to study, with some parents even filling in worksheets on their child’s behalf—going by the evidence of the handwriting.

“They have a hard time forcing the kid to answer modules because the kid isn’t intimidated by their parents,” she tells TIME. “The way a teacher encourages is very different from how a parent would.”

Consumo sometimes visits the homes of under-performing students and finds that they are out doing farm work—harvesting sugar cane, say, or making charcoal—to augment a family income that has been slashed by a suffering economy and a rising unemployment rate . Exercise books have been turned in blank, she says. Or students appear to pass their modules, only for her to find that they copied the answers. The frustration is enormous.

“It’s hard on our part,” Consumo tells TIME, “because we really try our best.”

Philippines Resumes Face To Face Learning After Two Years Of Covid-Related School Closures

Poverty and education in the Philippines

Internet access is a huge challenge. In urban areas, instructors can give lessons over video conferencing platforms, or Facebook Live, but 52.6% of the Philippines’ 110 million people live in rural areas with unreliable connectivity. It doesn’t come cheap either: research from cybersecurity firm SurfShark found that the internet in the Philippines is among the least stable and slowest, yet the most expensive, of 79 countries surveyed.

Internet access assumes, of course, that the user has a device, but in the Philippines that’s not a given. Private polling firm Social Weather Stations found that just over 40% of students did not have any device to help them in distance learning. Of the rest, some 27% were using a device they already owned, and 10% were able to borrow one, but 12% had to buy one, with families spending an average of $172 per learner. To put it into perspective, that’s more than half the average monthly salary in the Philippines.

“Some of them don’t have cell phones,” says Marilyn Tomelden, a teacher in Quezon province, three hours away from the Philippine capital Manila, who first noticed the digital divide when many of her sixth graders were unable to comply with what she thought of as a fun homework assignment: submitting videos of themselves performing dance moves she had demonstrated in an earlier video.

“Because we’re in public school, we cannot demand that they buy phones,” Tomelden says. “They don’t have money to buy their own food, and they’re going to buy their own cell phone for learning? Which is more important to live—to eat or to study?”

Instructors need to be equipped with the right resources too. A study from the National Research Council of the Philippines found that many teachers have had to shell out their own money to support their students in remote learning.

Read More: The Long History of Vaccinating Kids in School

Government agencies do what they can to help. Earlier this year, the customs bureau donated phones and other gadgets it had confiscated to the education department for distribution to needy students. But it’s a drop in the ocean.

“It’s something that is beyond [our] capacity to address—the inequality in terms of availability of resources of learners, depending on the socioeconomic status of families,” says education undersecretary San Antonio.

Some students are so exhausted by the struggle to study remotely that they are calling for long breaks between modules. Many parents and pressure groups are going even further, demanding total academic suspension until a clearer post-pandemic education system is ironed out.

Congresswoman France Castro is a member of ACT Teachers Partylist, a political party representing the education sector. She says a complete freeze would cause more problems than it solves.

“Education is a right,” she tells TIME. “Whatever form it will be, whether blended learning or modular, it’s better to continue it than to stop.”

But in the meantime, with their workloads multiplied, it is students and teachers paying the price. Consumo, the teacher from Januiay, regularly stays up late completing the reams of new paperwork generated by the distance learning system.

“You won’t be able to sleep anymore, just thinking about the deadlines and the work that still needs to be done,” she says. “I cry over that.”

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In last ASEAN country to reopen schools, Philippines' students fret about pandemic’s impact on their future

UN Philippines 2023 Annual Report

Secretary-General's Policy Brief on Education During COVID-19 and Beyond

Key Messages

The COVID-19 pandemic has created the largest disruption of education systems in history, affecting nearly 1.6 billion learners in all countries and all continents.
The pandemic has exacerbated education disparities. Learning losses due to prolonged school closures threaten to erase progress made in recent decades, not least for girls and young women .
Some 23.8 million additional children and youth (from pre-primary to tertiary) could drop out or not have access to school next year due to the pandemic’s economic impact alone.
Education is a fundamental human right. It is the bedrock of just, equal and inclusive societies and a main driver of sustainable development. To prevent a pre-existing learning crisis from turning into a learning catastrophe, governments and the international community must step up.
Once national or local outbreaks of the virus are under control, governments must look to reopen schools safely, listening to the voices of key stakeholders and coordinating with relevant actors, including the health community.
The gap in education financing globally could increase by 30% because of the crisis. Governments need to protect education financing in national budgets, in international development assistance and through greater cooperation on debt.
To cope better with future crises, governments should strengthen the resilience of education systems by placing a strong focus on equity and inclusion; and on reinforce capacities for risk management. Failure to do so poses major risks to international peace and stability.
The transformation of education systems has been stimulated and reinforced in many countries during the pandemic: innovative solutions for learning and teaching continuity have flourished.
Responses have also highlighted major divides, beginning with the digital one. It is time to reimagine education and accelerate positive change, and ensure that education systems are more flexible, equitable, and inclusive .
To spur global momentum around the education emergency and the need to protect and reimagine education in a post-COVID-19 world, a coalition of global organizations [i] is joining forces to launch the ‘ SaveOurFuture ’ campaign. This campaign will amplify the voices of children and young people and urge governments worldwide to recognize investment in education as critical to COVID-19 recovery.

[i] UNICEF, UNESCO, World Bank, Save the Children, Education Cannot Wait, Global Partnership for Education, Education Outcomes Fund, Education Commission, Asian Development Bank, African Development Bank.

View the Secretary-General's video statement.

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Save the Best for Children Under the Uncertainties of COVID-19

Too many children have been denied healthcare, been torn out of school, or left in abusive homes without access to protection.

Program: Child's Rights and Protection

Type: Story

Op-ed on associated with Policy Brief: “ COVID-19 pandemic: Lessons from the Asia-Pacific ”

Lessons from the Philippines:

The deadly widespread COVID-19 has brought unprecedented challenges to the world. It is not only a global public health emergency that has claimed thousands of hundreds of people’s lives, but also causes immediate and long-term economic impacts which have devastating effects on billions of households. The Philippine has become the second hardest-hit country by COVID-19 in Southeast Asia. As of early July, there are 51,754 confirmed cases and 1,314 fatalities reported. The number of confirmed cases has been increasing rapidly since June.

The most marginalized across the country– the 33 million children who make up around one-third of the Philippine population – are likely to be hit the hardest. Too many children have been denied healthcare, been torn out of school, or left in abusive homes without access to protection.

But in crisis there is also opportunity. The pandemic is a chance for regional governments to build back better, safer, and greener. In July, Save the Children set out a post-pandemic agenda for the Asia Pacific region, including the Philippines – a road map for how we can use the disruption of COVID-19 to create fairer and more inclusive societies. We believe that the virus must lead to a fundamentally different world – a new social contract between governments and people, drawing on lessons from the pandemic’s impact on all of our lives.

With the COVID-19 fatality rate of 3.37%, the Philippines has the second-highest fatality rate in Southeast Asia . The rapid spread and relative high death ratio of COVID-19 have shown the inefficiency of the healthcare system as well as insufficient public health management capacity of the Philippines. Broadly speaking, countries with well-functioning hospitals and stockpiles of crucial supplies including Personal Protective Equipment have done better in protecting their populations from the pandemic. The Philippines’ low healthcare expenditure mainly explains the low efficiency. The healthcare expenditure of the Philippines as part of the GDP has been around 4.4% for several years, much lower than the world’s average level of 9.89% . The country has also faced a shortage of vital medical supplies. “The average number of ventilators in small hospitals around the Philippines is very small compared to what is really needed,” according to a media report . Countries with better-resourced healthcare systems such as Thailand and Vietnam have done much better. For example, Thailand could provide 10,000 ventilators for a population of 70 million. The pandemic is a wake-up call for governments to target at least five percent of their GDP spending on healthcare moving forward.

The education sector has also been disrupted on an unprecedented scale, with 28,451,212 students affected in the Philippines due to the nation-wide school closures . To stem the spread of coronavirus, in early March, all educational institutions were enforced to close schools and classes have been shifted online . Philippine President Rodrigo Duterte recently said he will not allow students to go back to school until a coronavirus vaccine is available, even as some other countries resume in-person classes . However, virtual classrooms are inaccessible to those without internet connections. The pandemic has exposed the sharp digital divide in the Philippines, where most children living in rural and remote areas have no access to the internet. The Philippine’s network readiness index scores only 47.7, ranking 71 worldwide, compared with Singapore’s 82.13 , which shows that most Filipino children are not technically prepared for long-term online learning.

Internet access is becoming more than just a daily necessity but is also crucial in fulfilling a number of children’s human rights – including access to education and information. With online education likely here to stay, the Philippine government must redouble efforts to ensure that everyone can access the internet, including those in marginalized and rural communities.

The combination of lockdown and school closure has also heightened the risks of increased Violence Against Children (VAC), particularly online sexual exploitation in the Philippines. The financial and psychological pressures brought about by the pandemic have increased tensions in the home, resulting in huge spikes in calls to domestic violence hotlines in the country. Children have been particularly hard hit by what the UN has called a “shadow pandemic”, as they have been unable to access the protection services they normally would or find sanctuary and safety in schools. The Department of Justice (DOJ) Office of Cybercrime said 279,166 child sexual abuse cases have been reported from March 1 to May 24 this year, compared to 76,561 cases over the same period in 2019. The cases of internet-based sexual exploitation of children this year saw an increase of up to 264 percent, the Philippine’s DOJ pointed out .

The government leaders must use the pandemic to strengthen systems protecting children from domestic violence and other forms of abuse. They must invest in remote monitoring systems that can better detect violence against children behind closed doors in family homes. The virus has also shown that social service workers who play a crucial role in protecting children from harm must be deemed “essential” in the same way that medical doctors and nurses are.

The pandemic has wrought havoc across war-torn and wealthy societies worldwide. In the Philippines, the situation is not any better with 33 million children facing different forms of issues associated to healthcare, learning, and violence on different levels. We owe it to children to learn our lessons from the virus and create a world where they can not only stay alive with their families, but also grow and thrive.

John Hopkin University & Medicine https://coronavirus.jhu.edu/map.html visited on July 10, 2020. https://www.statista.com/statistics/678279/philippines-children-as-a-percentage-of-the-population/ Coronavirus (COVID-19) death rate in countries with confirmed deaths and over 1,000 reported cases as of July 1, 2020, by country. https://www.statista.com/statistics/1105914/coronavirus-death-rates-worldwide/ https://data.worldbank.org/indicator/SH.XPD.CHEX.GD.ZS?locations=PH-1W Ventilators for Covid-19 patients being produced by PHL experts https://businessmirror.com.ph/2020/05/01/ventilators-for-covid-19-patients-being-produced-by-phl-experts/ UNESCO, COVID-19 Impact on Education https://en.unesco.org/covid19/educationresponse COVID-19: Bangladesh shuts all educational institutions https://www.aa.com.tr/en/asia-pacific/covid-19-bangladesh-shuts-all-educational-institutions/1767425 Coronavirus: No vaccine, no school reopening in Philippines, Duterte says https://www.straitstimes.com/asia/se-asia/no-vaccine-no-school-reopening-duterte-says Disconnected: Digital divide may jeopardize human rights https://www.thejakartapost.com/news/2020/05/18/disconnected-digital-divide-may-jeopardize-human-rights.html Online child sexual abuse in Philippines triple during COVID-19 lockdown http://www.xinhuanet.com/english/2020-05/25/c_139087053.htm

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Academics sends messages of hope, inspiration and solidarity amidst COVID-19 pandemic

June 3, 2020 | 10:32 am

speech about covid 19 pandemic for students in philippines

Home / A-Team On-Line Publication / Academics sends messages of hope, inspiration and solidarity amidst COVID-19 pandemic

“I won’t let the Academic Community succumb to this COVID-19 pandemic. Learning continues. Teaching continues. Academic Service support continues. Staff support continues. Our operations continue for Learning knows no boundaries. Together, and by association, we shall all prevail”.

Juanito O. Cabanias, LPT, MAE, PhD Vice Chancellor for Academics

“At the end of the day all we need is hope and strength. Hope that it will eventually get better and strength to hold on till it does”. And while we are waiting; whatever hardship, challenges, indecisions and fears that we are facing, we just remember that these too shall pass and by the grace of God we will overcome. To our students we hope that they continue to trust us that what we do today will define what kind of health professionals they will all be in the future. We promise that excellence will still be part of everything that we do for them and our community. Just remember that tomorrow is still full of wonderful possibilities for all of us here in DLSMHSI.”

Alicia P. Catabay, RPh, MSc, PhD Dean, College of Pharmacy

“We are all in the gutter, but some of us are looking at the stars.” Oscar Wilde As a Lasallian community, may we continue to reach out to those who may have forgotten the light that shines above. There’s light in this darkness; all we need is to look up.”

Sigfredo B. Mata, RPh Vice Dean, College of Pharmacy

“Spanish flu (1918-1920); Asian flu (1957-1958); Hong Kong flu (1968-1969); H1N1/09 flu (2009-2010);

Which have lost an outrageous number of lives.

Then, COVID-19 (this too shall pass). Four words that may have given hope and resurrection to millions, then and now.

Life may be short, but God made sure it is wide and tall”

Jose Antonio P. Amistad, MD, FPSA Dean, The Student Affairs

“St. John Baptist de La Salle believes that education gives hope and opportunity for people. And so, during this time of COVID-19 uncertainty, let us remember that we were chosen to continue the mission of St. La Salle in nurturing the young, through education, especially those who had little hope for educational advancement due to COVID-19 pandemic.”

Marlon G. Gado, RL, MLIS Director, Center for Innovative Education and Technology Integration

“Weak? Tired? Feel like giving up? Take heart…We have Someone greater than all these challenges.

Isaiah 40:28-29, 31 says, “….The everlasting God, the Lord, The Creator of the ends of the earth, Neither faints nor is weary…. He gives power to weak, And to those who have no might He increases strength…. ….those who wait on the Lord Shall renew their strength;”

Maria Corazon E. Gurango, MD, MPH, FPAFP Director, Center for Community Engagement and Health Development Program

“This pandemic has caused anxieties, fears, and uncertainties. But we are capable, strong, and resilient. We may not be able to control the situation but we can control ourselves: our thoughts, actions, and choices. We can rise above these challenging times and continue our mission and ministry together as one Lasallian community.”

Efren M. Torres, Jr., RL, MLIS Director, Romeo P. Ariniego, MD, AFSC Library

“The COVID-19 pandemic brings new challenges unfolding each week, compelling us to re-configure our academic strategies with urgency but with uncertainty, and oftentimes beyond our capacity to cope. We must not lose hope, and as one community in Christ, continue with our mission inspired by John Baptist De La Salle.”

Lemuel A. Asuncion, OTRP OT Chair, Clinical Education College of Rehabilitation Sciences

“The Lord gives His toughest battles to His strongest soldiers”.

The greatest weapon we can have today is guarding ourselves with faith and prayer. May this situation help us realize that the Lord is always with us and will never leave us.”

Jion P. Dimson, RMT, MSMLS Chair, Student Development and Activities Department, The Student Affairs

“As Lasallians in these time of pandemia, let us compose ourselves as mature, responsible, and self-disciplined individuals minding our health and safety, as well as our academic responsibilities in achieving our full potentials. By simply staying at home and doing worthwhile things, we are expressing our reverence for life.”

Roberto L. Cruz III, RN, MAN Chair, Student Discipline and Security Department The Student Affairs

“As Lasallians we still continue to serve our partner communities despite the threats of COVID-19 along our way because nothing can stop our great desire to improve the health of our communities. We will continue to empower the people in the community for heath equity and for God’s greater glory!”

Jose Marcelo K. Madlansacay, MDC Chair, Community Service-Learning Projects Center for Community Engagement and Health Development Program

“The challenges of COVID-19 may seem unnerving yet, it can be an opportunity for growth and positive changes. May we strive to be hopeful, courageous and resilient despite the adversities we have to face. Let us draw strength from each other as we pray for healing around the world.”

Ma. Sheila Q. Ricalde, MAEd, RPm, RGC Vice Chair, Student Life The Student Affairs

“You are capable to handle this. I BELIEVE IN YOU. This health crisis can be overwhelming and that is PERFECTLY NORMAL. You may not think and/or feel at your best this time but THAT IS OKAY. YOU can still be a beacon of hope amidst this pandemic.”

Cesar M. Lago, MAEd, RGC Vice Chair, Student Success The Student Affairs

COVID-19: A Blessing?!

“COVID-19 can be considered as a blessing in disguise. Because of it, many have changed. All the busy streets were emptied, all the malls were closed, all were required to stay home. Everyone started to help one another, family ties were strengthened, everybody started again to turn to God and pray, pollution was lessened and Mother Earth started to recuperate. Let’s look on the brighter side. Stay well Lasallians!”

Jose Royce P. Aledia, RGC Chair, Student Wellness and Guidance and Counseling Department The Student Affairs

“As we face this time of uncertainty, the COVID-19 pandemic, do not forget that God is with us, we have to intensify our faith, be mindful that protection from sickness must begin within us, always wear mask, wash our hands frequently and observe distancing, take care not only our physical health but also our mental health. Keep safe everyone!”

Irene B. Maliksi, RN Chair, Student Health and Safety Department The Student Affairs

“There is light at the end of the tunnel, a rainbow after the rain and every cloud has a silver lining. Despite the pandemic, we, the Non-Teaching Personnel, will continue to support the administration in fulfilling the mission set forth by our Founder, St. John Baptist De La Salle. Together we will fight. Forever, we stand as ONE.”

Leslie V. Brito Administrative Assistant, College of Medical Laboratory Science

“Congratulations, graduating class of 2020! We might not have experienced the momentous march of our graduation, yet. Our victory should set blaze in our hearts and actions, not doused even by COVID-19. As the Wolff’s law says, “Strengthened under force of pressure, and overtime will become stronger to resist it.””

Christ Don E. Apuntar President, DLSMHSI Institutional Student Council

“As we face this global health crisis that causes major upheaval, we encounter challenges and become anxious about what’s going to happen. But surely, the presence of the Lord provides us hope of healing and certainty of the future. All you need to do is pray and strengthen your faith in Him! He will never leave us. He is our Emmanuel (God with us).”

“Even though I walk through the valley of the shadow of death, I will fear no evil, for You are with me; Your rod and Your staff, they comfort me”. —Psalms 23:4

Joren B. Fernecita President, College of Medicine Student Council

“We all know the dangers brought about by this pandemic, but worry less since everything is going to be alright. I do believe that we just need to have faith in God to overcome this. He will be the one to give us hope and strength to hold on until it gets better.”

Renee E. Andal President, College of Pharmacy Student Council

“To everyone, these are trying times but never forget to have faith in God and always seek guidance from Him. Always remember to stay safe and healthy. Let us fight this pandemic as One La Salle!

To our front liners, going above and beyond the call of duty is the essence of heroism. Some have fallen, but a lot more are continuing the fight. Heavy sacrifices have been, and continually being made. Words cannot capture the gratitude we wish to convey. But still, thank you front liners, heroes all, in every sense of the word.”

Christian Derik L. Aquino President, College of Humanities and Sciences Student Council

“COVID-19 has changed our way of living and has transformed us to something we have never imagined. This pandemic has affected all of us. As we face more challenges along our way, let us all be determined to keep on learning despite the hindrances. None of us can say with certainty when will it be safe…when will we get back to normal…when will this be over. Let not fear hold us back and may our faith help us triumph over this unseen enemy.

Stay safe. As Lasallians, We are One in this battle!”

Neil Vincent D. Guyamin President, College of Medical Imaging and Therapy Student Council

“As we face an invisible enemy, let’s take this opportunity to come together with clasped hands to pray for each and everyone’s safety. COVID-19 might have changed our way of viewing things, yet let’s be thankful for the life we have. At times like these, you may feel anxious; but do remember that you matter. You are not alone. We will bounce back and will continue to fight this pandemic as One Lasallian Family.”

Maria Veronica Louise C. Cabubas President, College of Medical Laboratory Science Student Council

“This COVID-19 pandemic has taught us a lot of things. We became closer to our family, realize the importance of our healthcare workers, and reflect that no one is safe unless you follow the guidelines set by our government. Even though we are far from its end, it is vital for us to always know that there is hope. That someday, we will see the light and overcome this darkness.

We shall conquer this together, Animo La Salle!”

Marvin Jay C. Salvador President, College of Nursing Student Council

“During his time, the Founder faced adversaries not too different from ours – poverty, hunger, sickness, corruption. He fully surrendered the work to God and succeeded. Likewise, let us remain steadfast in faith, bringing light to those who feel lost in the dark. Let us be vigilant whilst being empathetic to protect and nurture the weak and the weary. As we go through this pandemic, encourage a neighbor, check on a friend, hug a family member, donate to an organization, pray for the world; hope grows from the service we give – small or big. God will win the battle for us and we will call it our victory.”

Lizzy Jane Niquole Y. Ricardo President, College of Rehabilitation Sciences Student Council

“Yesterday is history, tomorrow is a mystery, today is a gift of God, which is why we call it the present.” ― Bill Keane

“Every day is a blessing and we should not forget to thank God for it. In this time of crisis, we need to strengthen our faith in Him and believe that this will soon be over. There’s no such thing as too much praying after all, for He can get us through this pandemic.”

Hennessy M. Frani President, SHSSHS Student Council

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Briones, education ministers unite to ensure learning continuity amid COVID-19

PASIG CITY, June 19, 2020 – True to their mission of ensuring that learning must continue, Secretary Leonor Magtolis Briones, together with other Southeast Asian education ministers, presented their different education strategies in response to the COVID-19 global crisis during the first South East Asian Ministers of Education Organization (SEAMEO) Ministerial Policy e-Forum held last Thursday, June 18.

With the theme, “Education in a Post-COVID-19 World”, the first SEAMEO e-forum provided a platform for education leaders and experts to share their knowledge and solutions on how to manage the effects of COVID-19 to the education landscape in Southeast Asia.

Secretary Briones and the education ministers from Brunei Darussalam, Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, Singapore, Timor-Leste, Thailand, and Vietnam, shared their education frameworks and innovations to frame the new normal in education and laid out their preparations for the opening of classes within their respective countries. Like the Philippines, other Southeast Asian countries have also adopted modular systems to deliver education while prioritizing the safety of the learners.

Singapore, who ranked second in all subjects among 78 countries in the 2018 Program for International Student Assessment (PISA), has continued to operate their schools amid the pandemic and is now starting their third term of classes.

“I think the basic choice before us is this – COVID-19 will be with us for some time, a year, and likely longer, until a vaccine is found. We cannot afford to keep schools closed for such a long time. It has a significant long-term impact on our children. It inflicts a tremendous social and human cost. Studies have shown that it can set students back for many years, even into adulthood. So we must try our best to save the school year, this and the next one, by keeping schools open but safe,” Singapore education minister Ong Ye Kung stated in his speech.

Minister Ong also mentioned that despite the challenges the education sector is facing, there were good things that came out from non-face to face strategies.

“It has been a tough period for every school system in the world. But there is a silver lining in every dark cloud. School systems in many countries have had to adjust to blended forms of learning in response to the pandemic, using the Internet, TV and even radio, as alternate platforms for students to gain access to education resources.”

The ministers also adopted a joint statement in the historic first ministerial e-forum as they shared progress made in fighting the COVID-19 pandemic and appreciated the efforts made by the educators and education stakeholders in member countries to ensure that no Southeast Asian learner is left behind especially in these unprecedented times.

No Filipino learner will be left behind amidst the crisis

As part of the Philippines’ short and long term strategies, Secretary Briones introduced the BE-LCP as a guideline for the department on how to deliver education in time of the COVID-19 pandemic while ensuring the health, safety, and welfare of all learners, teachers and personnel of DepEd.

“The first principle that we adhered to and which we are committed to, in compliance with the President’s directive is to protect the safety, health and well-being of our learners, teachers and personnel and to prevent further transmission of COVID-19. But at the same time, we want to ensure learning continuity. Our battle cry is learning must continue,” Briones said in the forum.

Another main feature of BE-LCP is the adoption of multiple learning delivery modalities, with blended learning and distance learning as major options.

Briones emphasized that online learning is only one option from the menu of learning modalities. These modalities will be offered appropriately depending on the situation of the learners’ households.

“We have come out with a variety, with menu of options, online is not the only answer, there’s a huge debate in the Philippines on how useful or whether it is really a good way of teaching learners, so we have online, we have televisions, we have radio. If all else fails, then learning modules are being printed so that these will be delivered in various pick-up points or either parents or for the village officials to distribute to the learners,” she said.

Title & authors

Ignacio, Aris E. "Online Classes and Learning in the Philippines During the Covid-19 Pandemic." International Journal on Integrated Education , vol. 4, no. 3, 2021, pp. 1-6, doi: 10.31149/ijie.v4i3.1301 .

Download citation file:

$Online Classes and Learning in the Philippines During the Covid\u002D19 Pandemic Image$

The COVID-19 pandemic brought great disruption to all aspects of life specifically on how classes were conducted both in an offline and online modes. The sudden shift to purely online method of teaching and learning was a result of the lockdowns that were imposed by the Philippine government. While some institutions have dealt with the situation by shutting down operations, others continued to deliver instructions and lessons using the Internet and different applications that support online learning. The continuation of classes online had caused several issues from students and teachers ranging from lack of technology to mental health matters. Finally, recommendations were asserted to mitigate the presented concerns and improve the delivery of the necessary quality education to the intended learners.

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Philippines Returns to School, Ending One of World’s Longest Shutdowns

More than two years after Covid emptied their classrooms, students are resuming in-person learning. The lost time will be hard to make up.

By Jason Gutierrez

MANILA — Millions of students throughout the Philippines headed to school on Monday as in-person classes began to fully restart for the first time in more than two years, ending one of the world’s longest pandemic-related shutdowns in a school system already plagued by severe underinvestment.

“We could no longer afford to delay the education of young Filipinos,” said Vice President Sara Duterte, who is also the education secretary, as she toured schools in the town of Dinalupihan, about 40 miles northwest of Manila.

Even before the pandemic, the Philippines had among the world’s largest education gaps, with more than 90 percent of students unable to read and comprehend simple texts by age 10, according to the World Bank.

Schools in the Philippines have long suffered from shortages of classrooms and teachers, whose pay is low, leaving the vast numbers of poor children who cannot afford private schools and rely on the public system with inadequate teaching.

Now, after losing more than two years of in-person instruction, schools face the monumental challenge of educating many students who have fallen even further behind.

Though the Philippines offered online instruction during the pandemic, many students lacked access to computers or internet connections, and overburdened parents often found it hard to keep tabs on their children’s remote learning.

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Successful COVID Campaign in Philippines Wins Accolades

November 2, 2020
By Stephanie Desmon

When the COVID-19 pandemic landed in the Philippines in March, it quickly became clear that there needed to be an easy way for the Department of Health to share important messages with its citizens on how to prevent the spread of the disease.

Working with the Johns Hopkins Center for Communication Programs, the Philippine Department of Health’s Healthy Pilipinas was quickly born. The Facebook page and COVID Alis sa Pamilyang Wais (“Family Smarts Keep COVID Away”) campaign went into action, sharing critical information on prevention behaviors such as handwashing, social distancing and mask wearing, as well as facts about testing, travel and much more.

The Facebook page has reached 160 million people since it started in late March. In the first month alone, the page earned 100,000 followers, with more than 270,000 new followers six months later. A single post in April reached 3.4 million people.

“The Healthy Pilipinas page is a one-stop source of credible, digestible and actionable information needed to fight the COVID-19 pandemic,” says Billie Puyat Murga, a senior program officer for social behavior change and gender, who led the effort for CCP. “The material is conveyed in a fun and conversational manner and is designed to reach household decision makers who can help protect their families.”

For this work, the team behind Healthy Pilipinas took home a Stevie Award, which recognizes the achievements of women executives, entrepreneurs and the organizations they run in the Philippines. The award came in the category of “Best Use of Social Media – COVID-19-related Information,” which honors nominations of social media communications, deployed for or by women in 2020, to inform the public about the pandemic and how to stay safe.

CCP’s COVID work in the Philippines is part of the Breakthrough ACTION project, funded by USAID, in 22 countries to assist with communication needs key to controlling the pandemic. The efforts have reached more than a billion people since March. The Philippines has recorded nearly 360,000 COVID-19 cases and nearly 6,700 deaths, according to Johns Hopkins University .

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v.11(3); 2021

Mental health and well-being of children in the Philippine setting during the COVID-19 pandemic

Grace zurielle c. malolos.

1 College of Medicine, University of the Philippines Manila, Manila, Philippines

Maria Beatriz C. Baron

Faith ann j. apat.

2 Matias H. Aznar Memorial College of Medicine, Cebu City, Philippines

Hannah Andrea A. Sagsagat

3 West Visayas State University-College of Medicine, La Paz, Iloilo City, Philippines

Pamela Bianca M. Pasco

Emma teresa carmela l. aportadera.

4 Faculty of Medicine and Surgery, University of Santo Tomas, Manila, Philippines

Roland Joseph D. Tan

5 Baguio General Hospital and Medical Center, Baguio City, Philippines

Angelica Joyce Gacutno-Evardone

6 Department of Pediatrics, Eastern Visayas Regional Medical Center, Tacloban City, Philippines

Don Eliseo Lucero-Prisno III

7 Faculty of Management and Development Studies, University of the Philippines Open University, Los Banos, Laguna, Philippines

8 Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, United Kingdom

The coronavirus disease 2019 (COVID-19) pandemic has subjected the mental health and well-being of Filipino children under drastic conditions. While children are more vulnerable to these detriments, there remains the absence of unified and comprehensive strategies in mitigating the deterioration of the mental health of Filipino children. Existing interventions focus on more general solutions that fail to acknowledge the circumstances that a Filipino child is subjected under. Moreover, these strategies also fail to address the multilayered issues faced by a lower-middle-income country, such as the Philippines. As the mental well-being of Filipino children continues to be neglected, a subsequent and enduring mental health epidemic can only be expected for years to come.

Introduction

The Philippine Development Plan for 2017-2023 highlights that children are among the most vulnerable population groups in society, including them in strategies for risk reduction and adaptive capacity strengthening. 1 Approximately 40% of the total Philippine population is comprised of Filipinos below 18 years of age. 2 Despite having a large portion of the Philippine population declared as vulnerable, concerning issues involving them still persist and remain unaddressed.

Among Filipino children aged 5 to 15, 10% to 15% are affected by mental health problems. 3 According to the World Health Organization (WHO), 16.8% of Filipino students aged 13 to 17 have attempted suicide at least once within a year before the 2015 Global School-based Student Health survey. 4 This is just one of the many indicators showing the state of mental health of these children. These statistics involving children’s mental health are concerning as childhood is a crucial period where most mental health disorders begin. Efforts should be made to identify these issues early for proper treatment in prevention of negative health and social outcomes. 4 Childhood mental and developmental disorders also frequently persist into adulthood, making it more likely for them to have compromised growth with greater need for medical and disability services and higher risk of getting involved with law enforcement agencies. 5 In this context, the COVID-19 pandemic threatens to worsen these numbers, affecting the delivery of the Philippines’ health care services, including those for children’s mental health.

Since the beginning of the pandemic, children have been subjected to multiple threats to their mental health. Adding insult to injury, several concurrent factors in the Philippine society exacerbate this. While these are experiences shared by all people regardless of age, impediments to emotional and social development are greater in children than in adults. 6 They may also be more vulnerable to developing mental health issues such as depression and anxiety. 7 Together with these circumstances and the weakened health care system, children’s vulnerability towards mental health problems may be worsened by the pandemic, leading to more new cases and exacerbating existing ones. 2

Status of mental health system for children in the Philippines

According to the National Statistics Office (NSO), mental health illnesses rank as the third most common form of morbidity among Filipinos. 8 In the assessment conducted on the Philippine mental health system, a prevalence of 16% of mental disorders among children was reported. 9 With this alarming number of cases, it is surprising to see how the Philippines is currently responding to this problem. To date, there are only five government hospitals with psychiatric facilities for children, 84 general hospitals with psychiatric units, and 46 outpatient facilities from which there are only 11 that are designated for children and adolescents. Additionally, there are only 60 child psychiatrists practicing in the Philippines, with the majority of them practicing in urban areas such as the National Capital Region. Hence, children with mental health problems who are in rural areas have less access to such services. 10

As the pandemic continues, combined with the menace of the typhoon season, thousands of children are placed in a situation where the future is uncertain. A local study showed that youth age and students are among those with significant association to a greater psychological impact due to the pandemic. 11 In addition, UNICEF also reports that children nowadays face a trifecta of threats which include direct consequences of the disease itself, interruption in essential services, and increasing poverty and inequality. All of these can lead to higher incidences of stress, anxiety, and depression. 12

General mental health implications of COVID-19 on Filipino children

The fear and anxiety of contracting the virus, the suspension of physical classes, the disruption of regular daily routine, and the decrease of social support from school peers collectively add burden to the mental well-being of children. 7 , 13 The shift to online classes increases the burden on the mental well-being of children. Excessive use of these technologies has been associated with developmental delays and has resulted in sleep schedule disruptions. 14 This situation is aggravated by the strict implementation of the confinement of children at home. Children living with preexisting mental health concerns, 13 and living in cramped households and communities face worse circumstances.

Militarization of the Philippine COVID-19 response

Aside from being regarded as one of the countries with the longest lockdown, the Philippines has also been called out by the United Nations for employing a highly militaristic approach in response to the COVID-19 pandemic. 15 Militarization may come across as threatening, because it implies a potential for violence. 16 Furthermore, few studies abroad have reported that children and adolescents may tend to view police forces as punitive figures whom they fear. 17 , 18 While these qualitative studies were conducted long before the current health crisis began, it may be possible for increased military presence in communities to exacerbate the fears already emanating from the pandemic itself; this can negatively impact a child’s psychological development. 4 Still, local evidence to confirm these associations, especially in the context of the pandemic, is lacking. Many studies have already documented the impact of lockdown on children, but none of them have looked into how the strategies for implementation may also be contributory to their mental health or well-being.

Typhoons and the mental health of Filipino children

The Philippines has been hit by 22 tropical typhoons during the COVID-19 pandemic, leaving thousands of families homeless. 19 Children who are already frightened of COVID-19 and previous tropical storms have had to relive their experience with each new typhoon that came. In addition, children in crowded evacuation centers are at increased risk of contracting diseases and experiencing gender-based violence. 20 Given how past typhoons of similar strength and destruction have caused lasting adverse mental health effects on children, 21 the same or even worse, may be expected as a result of the more recent calamities. Super typhoons Goni and Vamco have caused further disruptions in schooling and livelihood, therefore leaving more children vulnerable to the effects of the pandemic. Those who have been forced to seek refuge in evacuation centers are at an increased risk of acquiring COVID-19, among other diseases. 20

Child Labor and Abuse in the Time of COVID-19

The COVID-19 crisis caused an unprecedented reduction in economic activity and working time, thus increasing poverty. Fewer employment opportunities and lower wages drive exploitative work. Further suppression of wages induces child labor. There may be deliberate recruitment of children to cut costs and boost earnings. 22

In addition to the threats of child labor, a study entitled The Hidden Impact of COVID-19 on Children reported that violence occurred in nearly one-third (32%) of households. Lesser household incomes were associated with more reports of violence towards children. 23 According to UNICEF, the Philippine government saw a 260% increase in online child abuse reports from March-May. Many victims are first abused by their parents, who livestream sexual violence for predators in wealthy Western nations. This occurrence resulted from job and income loss and more time spent at home due to strict quarantine measures. The abuse in children occurs at an average of 2 years before being rescued. 24

Strategies Addressing the mental health implications of COVID-19 on Filipino children

Numerous strategies have been utilized to address the mental health impacts of COVID-19 on Filipinos. With the mental health implications predicted at the beginning of the pandemic, the Psychological Association of the Philippines has compiled a list of free telemedicine consultations. As of August 24, the Philippine Red Cross has also established a COVID-19 hotline with 9790 helpline volunteers to address mental health and other similar concerns. The Department of Health has also conducted nationwide campaigns in observance of the National Mental Health Week. 25

Albeit present, these interventions are limited to the general population, and strategies specific to addressing the mental health situation of children remain scarce and staggered. Compounding factors of classifying among the lower- to middle-income countries of militarization, natural disasters, and child labor and abuse have yet to be considered. In addition, it is also important to consider that happiness, with its multifactorial nature, is a vital component of an individual’s overall wellbeing. 26

The already-challenged state of mental well-being of Filipino children has been worsened by the pandemic and the lack of good mental health policies by the government. While there is increasing awareness for mental health, children-centered interventions remain deficient. Approaches must integrate commonly-known mental health effects on children with existing and anticipated Philippine societal issues. Without doing so, it may be expected that as the COVID-19 pandemic is mitigated, a mental health epidemic will replace it.

Competing interests

The authors have no conflicts of interest.

Ethical approval

Not applicable.

Authors’ contributions

GZCM and DELP were involved in the conception of the paper. GZCM led the writing of the manuscript and acted as corresponding author. GZCM, MBCB, FAJA, HAAS, PBMB, ETCA and RJDT wrote sections of the manuscript. AJGE and DELP reviewed and edited the initial draft of the manuscript prior to submission. All authors have reviewed and agreed to the final version of the paper.

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2 Minute Speech on Covid-19 (CoronaVirus) for Students

The year, 2019, saw the discovery of a previously unknown coronavirus illness, Covid-19 . The Coronavirus has affected the way we go about our everyday lives. This pandemic has devastated millions of people, either unwell or passed away due to the sickness. The most common symptoms of this viral illness include a high temperature, a cough, bone pain, and difficulties with the respiratory system. In addition to these symptoms, patients infected with the coronavirus may also feel weariness, a sore throat, muscular discomfort, and a loss of taste or smell.

10 Lines Speech on Covid-19 for Students

The Coronavirus is a member of a family of viruses that may infect their hosts exceptionally quickly.

Humans created the Coronavirus in the city of Wuhan in China, where it first appeared.

The first confirmed case of the Coronavirus was found in India in January in the year 2020.

Protecting ourselves against the coronavirus is essential by covering our mouths and noses when we cough or sneeze to prevent the infection from spreading.

We must constantly wash our hands with antibacterial soap and face masks to protect ourselves.

To ensure our safety, the government has ordered the whole nation's closure to halt the virus's spread.

The Coronavirus forced all our classes to be taken online, as schools and institutions were shut down.

Due to the coronavirus, everyone was instructed to stay indoors throughout the lockdown.

During this period, I spent a lot of time playing games with family members.

Even though the cases of COVID-19 are a lot less now, we should still take precautions.

Short 2-Minute Speech on Covid 19 for Students

The coronavirus, also known as Covid - 19 , causes a severe illness. Those who are exposed to it become sick in their lungs. A brand-new virus is having a devastating effect throughout the globe. It's being passed from person to person via social interaction.

The first instance of Covid - 19 was discovered in December 2019 in Wuhan, China . The World Health Organization proclaimed the covid - 19 pandemic in March 2020. It has now reached every country in the globe. Droplets produced by an infected person's cough or sneeze might infect those nearby.

The severity of Covid-19 symptoms varies widely. Symptoms aren't always present. The typical symptoms are high temperatures, a dry cough, and difficulty breathing. Covid - 19 individuals also exhibit other symptoms such as weakness, a sore throat, muscular soreness, and a diminished sense of smell and taste.

Vaccination has been produced by many countries but the effectiveness of them is different for every individual. The only treatment then is to avoid contracting in the first place. We can accomplish that by following these protocols—

Put on a mask to hide your face. Use soap and hand sanitiser often to keep germs at bay.

Keep a distance of 5 to 6 feet at all times.

Never put your fingers in your mouth or nose.

Long 2-Minute Speech on Covid 19 for Students

As students, it's important for us to understand the gravity of the situation regarding the Covid-19 pandemic and the impact it has on our communities and the world at large. In this speech, I will discuss the real-world examples of the effects of the pandemic and its impact on various aspects of our lives.

Impact on Economy | The Covid-19 pandemic has had a significant impact on the global economy. We have seen how businesses have been forced to close their doors, leading to widespread job loss and economic hardship. Many individuals and families have been struggling to make ends meet, and this has led to a rise in poverty and inequality.

Impact on Healthcare Systems | The pandemic has also put a strain on healthcare systems around the world. Hospitals have been overwhelmed with patients, and healthcare workers have been stretched to their limits. This has highlighted the importance of investing in healthcare systems and ensuring that they are prepared for future crises.

Impact on Education | The pandemic has also affected the education system, with schools and universities being closed around the world. This has led to a shift towards online learning and the use of technology to continue education remotely. However, it has also highlighted the digital divide, with many students from low-income backgrounds facing difficulties in accessing online learning.

Impact on Mental Health | The pandemic has not only affected our physical health but also our mental health. We have seen how the isolation and uncertainty caused by the pandemic have led to an increase in stress, anxiety, and depression. It's important that we take care of our mental health and support each other during this difficult time.

Real-life Story of a Student

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Harnessing ai speech recognition technology for educational reading assessments amid the covid-19 pandemic in the philippines [cies 2024 presentation].

Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines

Research Article
Published: 20 October 2020
Volume 3 , pages 445–468, ( 2020 )

Cite this article

Joshua Uyheng ORCID: orcid.org/0000-0002-1631-6566 1 &
Kathleen M. Carley 1

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Online hate speech represents a serious problem exacerbated by the ongoing COVID-19 pandemic. Although often anchored in real-world social divisions, hate speech in cyberspace may also be fueled inorganically by inauthentic actors like social bots. This work presents and employs a methodological pipeline for assessing the links between hate speech and bot-driven activity through the lens of social cybersecurity. Using a combination of machine learning and network science tools, we empirically characterize Twitter conversations about the pandemic in the United States and the Philippines. Our integrated analysis reveals idiosyncratic relationships between bots and hate speech across datasets, highlighting different network dynamics of racially charged toxicity in the US and political conflicts in the Philippines. Most crucially, we discover that bot activity is linked to higher hate in both countries, especially in communities which are denser and more isolated from others. We discuss several insights for probing issues of online hate speech and coordinated disinformation, especially through a global approach to computational social science.

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Introduction

In the time of COVID-19, nations all over the world face not just a major public health crisis, but also a crisis of social relations [ 66 , 82 ]. Especially in settings of entrenched inequalities and political polarization, the pandemic has exposed and exacerbated conflicts between social groups [ 31 , 51 ]. In this work, we investigate how such dynamics play out in cyberspace. We specifically examine the phenomenon of hate speech on social media, especially in relation to online disinformation [ 10 , 20 , 59 ]. In the context of a global pandemic, we ask to what extent the spread of online hate speech may be linked to bot-driven activities. We also probe what ends such information maneuvers may be instrumentalized toward, with consequences which extend beyond the digital sphere [ 5 , 17 , 70 , 86 ].

This work bears several implications for understanding online hate speech in the context of the pandemic and beyond. We pivot from extant technical approaches of classifying hate speech [ 7 , 35 , 53 ], to theory-informed frameworks for characterizing it in the context of large-scale social interactions and potential information maneuvers [ 44 , 80 , 84 ]. We also reflect on the value of taking a global approach to computational social science, especially in the context of international issues like COVID-19, with its universal yet also distinct and unequal impacts in societies worldwide [ 31 , 51 , 54 ].

In the succeeding sections, we offer an overview of related work in this area to lay the conceptual foundations for this paper. First, we discuss the problem of hate speech on social media. We highlight the importance of shifting from the prevailing concern with classification to applied settings of characterization [ 80 , 84 ]. Second, we link our view of hate speech to the literature on bots and information maneuvers. We specifically situate our work within the emerging field of social cybersecurity, which underscores the multidisciplinary nature of looking at disinformation in terms of sociotechnical systems [ 11 , 15 , 22 ]. Finally, we zero in on the present context of the global pandemic. We frame existing theoretical and methodological frameworks in terms of our real-world case studies of the United States and the Philippines, two countries which face rampant public health and social issues in the time of COVID-19 [ 26 , 82 ].

Related work

Hate speech on social media: from classification to characterization.

Hate speech has been broadly defined as abusive language that targets a specific group [ 30 ]. On social media, hate speech further proliferates in terms of highly connected ‘highways’, such that different forms and communities of hate intensify each other [ 21 , 44 ]. Online hate speech, thus, goes beyond a merely linguistic phenomenon. It is also grounded in real-world divisions [ 64 , 67 ]. Conversely, it also has real-world impacts. Empirical research shows that online hate can predict offline hate, especially against minorities like Black and Muslim populations [ 5 , 86 ]. Probing the psychological processes underlying these relationships, scholars have also found that repeated exposure to hate speech may increase levels of prejudice by making individuals desensitized to derogatory and exclusionary narratives about certain groups [ 50 , 70 ]. These processes consequently lead to broader strains on intergroup relations and heightened political polarization [ 17 ].

Identifying hateful online behaviors, thus, represents an important task toward safeguarding the health of digital platforms and supporting community-based responses to countering toxicity [ 38 , 55 ]. Major efforts from a computational and natural language processing standpoint have focused on the task of automatically classifying hate speech [ 25 , 35 , 85 ]. Extensive progress has been made with regard to harnessing cutting-edge deep neural models to predict hate speech in text with a high degree of accuracy [ 7 ]. For example, innovative recent work trains word embedding approaches to account for the specific language of ‘othering’ used in hate speech to further improve prediction performance [ 2 , 32 ].

Emerging literature in the social and computational sciences increasingly adopts the view that interpretability of models is likewise important [ 53 ]. In certain cases, the complex features of state-of-the-art neural models may make it challenging for researchers to understand the predictions made. From this standpoint, valuable approaches have also looked at simpler models which rely on theoretically enriched and engineered features. For instance, a valuable research area in psycholinguistics documents systematic and cross-cultural links between various patterns of word usage with behaviors and mental states [ 63 , 76 ]. By employing more interpretable features, it becomes more straightforward to describe the social and communicative dynamics which underpin hate speech as it is used in context. This enables researchers to better understand not just which texts may be linked to hate speech, but also how they communicate hate, evolve in communities, and reinforce conflicts [ 3 , 33 , 47 , 50 ].

Hate speech in bot-driven information maneuvers

Building on this broader research agenda of hate speech characterization, we further situate its spread within the context of potential information operations. Although digital platforms have often been hailed for their capacity to democratize public communication, they have also been scrutinized for the ways they facilitate disinformation by inauthentic actors such as social bots [ 10 , 59 ]. Especially in recent years, social bots have played a noteworthy role in proliferating digital content pollution across a variety of contentious settings [ 34 , 38 , 69 ].

Given their ubiquity, social bots have also been defined in heterogeneous ways. A major definition in this area refers to bots as algorithms designed to automatically generate content and interact with human users [ 34 ]. Scholars note that many kinds of social bots display distinctive behavioral patterns to achieve concerted political or economic ends, including spamming, increasing the perceived following of a politician, or sowing discord [ 83 , 87 ]. The literature also points out that some bots—sometimes called cyborgs—are only partially automated, such that certain messages they send can attain a higher level of sophistication due to intermittent interventions by humans during real-time interactions [ 27 , 80 ]. For the purposes of this work, and in line with the operationalizations embedded in tools described in Sect. 3.3 , we align with this broadest sense of a social bot. Hence, social bots in this study refer to accounts using at least some level of automation to achieve informational objectives in the context of online social networks.

Social cybersecurity presents a computational social science framework for organizing the activities of social bots around specific information maneuvers [ 22 ]. Information maneuvers are broadly divided into two categories: narrative and network maneuvers [ 11 , 14 , 15 ]. These are further subdivided into specific positive and negative strategies under the BEND framework as a heuristic. Narrative maneuvers have to do with shaping the way certain storylines play out in public discourse. Coordinated actors may seek to positively reinforce or negatively shut out certain perspectives in the online conversation [ 61 , 75 ]. Positive maneuvers include the engagement of related storylines, the further explanation of a topic, or the excitement and enhancement of a group (E). Negative maneuvers, on the other hand, seek to dismiss storylines, distort messages, or dismay and distract the concerned group (D).

Network maneuvers, on the other hand, aim to influence the structural features of social media interactions. By altering who talks to whom, information maneuvers may strategically shape the flow of information regardless of its content [ 8 , 37 , 57 ]. Bots targeting such objectives may seek to make certain actors more influential or less influential. They may also aim to break up a community or inorganically link two communities together [ 4 , 83 ]. These notions correspond to the positive maneuvers summarized as backing, boosting, bridging, and building (B); and negative maneuvers given by neutralizing, nuking, narrowing, and neglecting (N). Such maneuvers are instrumentalized toward diverse ends, including manipulating public opinion and exacerbating political unrest [ 16 , 43 , 78 , 78 ].

This paper, thus, advances a view of hate speech as a complex social phenomenon which may be embedded in information maneuvers. Without discounting that the broader public may organically engage in hateful talk online [ 71 , 72 ], the evidence that bots exert significant influence in driving digital toxicity nonetheless makes it important for researchers to examine their potential impacts in the context of the pandemic [ 74 , 79 ]. For instance, from a BEND perspective, bots can promote networked hate by building hate groups and backing hate-promoting opinion leaders. In addition, they could shape narratives through various maneuvers such as distort or dismay to increase the volume of messages spreading hate. We therefore conceptualize bot-driven hate in line with an analytical perspective harnessing both the computational and social sciences [ 49 , 52 , 82 ].

Global hate during the pandemic: The United States and the Philippines

Against the backdrop of the COVID-19 pandemic, the foregoing insights become important on a global scale. Emerging studies have looked at how the pandemic has fueled racialized and xenophobic tensions [ 31 , 51 ]. In particular, because the first notable outbreaks happened in China, it has become a major concern whether online talk stokes sinophobia [ 73 , 89 ]. Months into the pandemic, the objects of conflict have also evolved, accounting for the politically heated landscape of institutional pandemic response and growing disparities in equitable treatment delivery [ 42 , 54 ].

Within specific geopolitical landscapes, the dynamics of online hate and disinformation may vary. In the United States, robust scholarship tackles the potential influence of foreign influence campaigns [ 6 , 9 ]. Known operations, for example, have stoked online conflicts in relation to wider racial divisions [ 4 , 89 ]. Meanwhile, in the Philippines, international tensions with China may also play a role in shaping racially charged online hate [ 58 , 78 ]. However, in view of well-documented evidence that disinformation in the Philippines is primarily domestic and state-sponsored, information maneuvers may also play a role in primarily political conflicts [ 62 , 79 ].

Common between the two countries, however, are a combination of hyperpartisanship among polarized publics, the rise in populist-authoritarian leadership, and sustained challenges in curbing the pandemic [ 1 , 68 , 81 ]. At the time of writing in mid-September, the US faces the largest number of COVID-19 cases in the world, while the Philippines has some of the highest in Southeast Asia [ 88 ]. Taken together, these factors may contribute to the proliferation of hate speech during a volatile political and economic historical moment, especially towards locally marginalized groups.

In this work, we seek not only to characterize hate speech in the context of potential bot-driven activities, but also to compare their dynamics across two distinct geopolitical settings. This affords us a more holistic and diverse view of the digital landscape amid a planetary crisis like COVID-19. It also helps us advance a more complete practice of computational social science in extending beyond the predominant focus on Western, Educated, Industrialized, Rich, and Democratic nations (WEIRD) [ 39 , 41 ]. These bear implications we discuss further in our concluding sections.

Data and method

Data collection.

Online conversations around the COVID-19 pandemic were collected using Twitter’s REST application programming interface (API) updated on a daily basis. We used search terms about the pandemic in the Philippines and the US. Localized hashtags were used to delineate tweets of interest, specifically ‘#COVID19PH’ for the Philippines and ‘#COVID19US’ for the US. To enforce reasonable comparability between the datasets, hashtags which were not geographically specific to either the US or the Philippines were not included, such as #Wuhanvirus, #Chinavirus, and #coronavirus, which related studies have otherwise used [ 36 , 89 ]. In this manner, we sought to delineate our study to discourse surrounding the mainstream, country-specific hashtag related to the topic. For the comparative purposes of our study, all data on the US and all data on the Philippines were processed in parallel; that is, datasets were treated separately from each other with comparisons drawn after analytical strategies were implemented.

We continued data collection over a 75-day period from March 5 to May 19 of 2020. For parsimony, data were strategically time-bound before the emergence of the ‘#BlackLivesMatter’ protests, which also impacted public discourse worldwide. We stored user metadata, tweet metadata, data on user interactions, and data about the hashtags and URLs each tweet used. Twitter interactions in this work collectively refer to the sum of retweets, replies, mentions, and quotes. Retweets were included to capture the intuition that they account for the amplification of certain messages over others. Hashtags were also retained for all subsequent textual analysis.

The final datasets consisted of 12 million tweets featuring 1.6 million users in the US, and 15 million tweets featuring 1 million unique users in the Philippines. Larger datasets have been collected with more universal and dynamically updated search terms [ 24 ]. However, for the purposes of this work, it was important to hold constant the geographically specific nature of the conversation; thus, we maintained this smaller, yet more theoretically appropriate dataset for analysis [ 60 ]. It is important to note that state-level terms were not included in the US collection stream; thus, the data on the US may represent a smaller fraction of American pandemic talk, focusing on people discussing the pandemic in relation to the nation at large. Future work may probe whether the bot-driven hate dynamics found here extend more broadly.

Hate speech classification

Hate speech scores were assigned to each tweet using a machine learning algorithm. For user-level analysis, we obtained the average hate speech score assigned to all tweets by the account within the time interval under consideration. Our model utilized handcrafted linguistic features using the Netmapper software [ 11 , 79 , 80 ]. These features included lexical counts of pronouns, abusive terms, exclusive terms, absolutist terms, and identity terms, among others based on prior scholarship linking language use to psychological states [ 63 , 76 ]. Second-order interactions were also added to capture the co-occurrence of various linguistic features in a given tweet. We performed this feature enrichment in a multilingual setting, capturing counts for English words as well as common languages in the Philippines (e.g., Tagalog, Cebuano).

Our model was trained on a seminal benchmark dataset for hate speech [ 30 ]. This dataset had three labeled classes for training in a supervised setting: (a) hate speech, (b) offensive speech, and (c) regular speech. A crucial consideration here is the distinction between hate speech and offensive speech, as the latter may include abusive terms which are not necessarily targeted toward any specific groups. By generating predictions for both labels, we mitigate false positives. However, we note that this may also conversely impose overly stringent predictions for hate speech, resulting in some level of underprediction [ 30 ]. For this purpose, we also consider predictions of offensive language in some of our analysis.

Across several experiments, the best model was a random forest classifier achieving over 80% performance in terms of micro-averaged and weighted F1 to account for class imbalance. For the same reason, training was also performed with oversampling on the training set. While more performant models could be designed using state-of-the-art neural network architectures, we followed practical arguments in recent work advocating interpretability to perform hate speech prediction at scale with theoretically motivated features [ 53 ].

Table 1 summarizes the performance of our chosen model relative to a random baseline, a heuristic baseline, and a regularized logistic regression baseline. The random baseline was evaluated as the average performance obtained using a random permutation of true labels. Anchored in the conceptual definition of hate speech [ 30 ], the heuristic baseline is set up as follows: we predict hate speech if there is at least one abusive term and one identity term, we predict offensive language if there is at least one abusive term but no identity terms, and we predict regular speech otherwise. For logistic regression, we tested different values for the regularization parameter. For the random forest model, we tested different numbers of estimators from 5 to 100 in increments of five.

Bot prediction

Bot scores were also assigned to each user using the BotHunter algorithm [ 11 , 13 ]. BotHunter also relies on a random forest classifier trained on several labeled datasets of known social bots. Adopting a tiered structure, BotHunter progressively leverages account information, network information, and dynamic features to generate performant bot predictions. It has been successfully used to understand various domestic and international influence campaigns [ 14 , 16 , 78 , 80 ].

BotHunter was the preferred bot prediction algorithm in this study due to its scalability and reliable accuracy over large and diverse datasets. In the context of this study, we do not retrain the BotHunter model. However, BotHunter uses a variety of annotated examples from diverse domains, including a bot attack on NATO, known Russian accounts associated with the Internet Research Agency (IRA) which interfered in the 2016 US elections, and a large dataset of known suspended accounts [ 12 ]. Experiments show good domain generalizability across a variety of different types of bots in different contexts. Detailed training specifications and generalized performance on different domains are examined in prior work, but are beyond the scope of the present study [ 11 , 12 ].

Identity analysis

To determine the targets of hate speech, we employed an identity-based approach [ 17 , 50 ]. Past research had developed a comprehensive lexicon of identity terms which have also been made available on the Netmapper software [ 23 , 45 ]. Each identity term in the lexicon was further subdivided into four classes: political identities (e.g., senator, president), gendered identities (e.g., women, transgender), racial/nationality identities (e.g., Black, Filipino), and religious identities (e.g., priest, imam). Subclasses were based on related work on hate speech and social media identity prediction more generally [ 46 , 65 ]. Acknowledging that identities are intersectional [ 28 ], this subcategorization scheme was set up such that each identity term could be assigned any combination of the four identities mentioned above. Each tweet was also assigned an identity score based on the number of times it invoked each subclass of identity term.

Social network analysis

We use the ORA software to conduct social network analysis on our datasets [ 23 ]. For a given time t , we represented the Twitter conversation for a given country c using a graph $G_t^c = \left( V_t^c, E_t^c\right)$ . For the purposes of the present analysis, we divided our data into daily datasets. Here, $V_t^c$ represents the set of Twitter accounts represented in the data at the specified time, which act as nodes in the network. Meanwhile, $E_t^c$ represents the edges between nodes, representing the interactions between users. Edges were weighted by the sum of all Twitter interactions within the time period consisting of quotes, mentions, replies, and retweets.

Network influence

We used the network representations described above to obtain measures of degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality using the ORA software [ 11 , 19 , 40 ]. Centrality metrics on social networks capture varied notions of how important each actor is in the spread of information.

Total degree centrality in this context captures the extent to which users are engaged in all kinds of Twitter interactions. Betweenness centrality denotes how much accounts act as bridges, thereby connecting relatively distinct groups. Eigenvector centrality corresponds to the degree to which certain accounts interact with other influential accounts. Finally, closeness centrality gives us an idea about how much agents control overall information flow in the network. Results pertaining to network influence paint a holistic picture of whether more hateful users were successful in accruing network influence, especially if they were more bot-like.

Cluster analysis

Finally, to view hate speech and bot activity in the context of localized interactions, we performed community detection using the Leiden algorithm [ 77 ]. The Leiden algorithm advances the state of the art in network clustering over the more commonly used Louvain method [ 18 ]. Intuitively, it aims to partition a given social network into smaller groups such that agents in the same cluster are more likely to interact with each other than with agents from other clusters. With guarantees to produce well-defined clusters, the Leiden algorithm automatically selects the optimal number of communities in a network. It also boasts faster runtime even with large datasets.

Localizing our analysis in terms of Leiden groups conceptually aligns with broader understandings of social media dynamics. Despite the large-scale nature of online conversations, most agents are exposed only to information within specific communities [ 8 , 37 , 79 ]. Thus, it is valuable to examine how bot activities and hate speech co-vary on the cluster level alongside the global analysis offered by the centrality measures discussed above.

Clusters may additionally be characterized by a variety of metrics. Size is a basic measurement of how many agents have been assigned to a given cluster. The E/I index provides a measure of how exclusively agents in a cluster interact with each other versus those in other clusters [ 48 ]. Values closer to +1 indicate that the cluster interacts with agents outside their cluster; whereas values closer to -1 suggest that agents are siloed in their interactions. Finally, the Cheeger constant measures the amount of bottleneck behavior, with higher values indicating that information may be concentrated among a smaller number of nodes relative to the rest [ 56 ]. We implement several regression models to determine the relationship of these community features with bot activity and hate speech.

Overview of bot and hate scores

Our first set of results presents BotHunter and hate speech predictions on our two datasets. Table 2 suggests that levels of bot activity and hate speech are comparable across the two contexts. Using a standard 80% BotHunter threshold [ 78 , 79 ], we note that while a larger proportion of tweets are produced by bots in the US dataset, we captured more bot tweets in raw numbers in the Philippine dataset. Conversely, while a larger number of bot users were found in raw numbers in the US, the Philippines had a greater proportion of bot users. Collectively, these initial predictions suggest that bots were tweeting at a more prolific rate in the Philippines compared to the US.

Table 2 shows the mean scores obtained for both hate speech and offensive speech. Average scores were consistently higher in the US for hate speech and offensive speech, both on the tweet level and on the user level. Overall, however, hate speech and offensive speech distributions are skewed to the right, although the average value for offensive speech is higher than that of hate speech. This indicates that, despite the relatively neutral choice of search terms used during data collection, offensive talk is relatively common in both datasets. Cases of hate speech with high probability, however, are quite rare [ 89 ].

Four months after data collection, we found that a notable percentage of accounts were no longer active on Twitter. Suspensions accounted for 5.96% of the accounts in our US dataset, and 5.18% of the accounts in our Philippine dataset. Offering some validation for our model predictions, Welch two-sample t tests established that in the US, suspended accounts had higher BotHunter probabilities ( $t = 41.82, p < 0.001$ ), and higher scores on offensive speech ( $t = 8.04, p < 0.001$ ). Interestingly, no significant difference was found in levels of hate speech between suspended and non-suspended accounts in the US. Meanwhile, in the Philippines, we saw that suspended accounts consistently had higher BotHunter probabilities ( $t = 18.78, p < 0.001$ ), hate speech scores ( $t = 6.55, p < 0.001$ ), and offensive speech scores ( $t = 9.43, p < 0.001$ ).

Coefficients of a logistic regression model predicting whether an account is suspended based on its BotHunter probability, its hate speech score, and its offensive speech score. Error bars represent 95% confidence intervals

Taken together, we observe different patterns between the two datasets in predicting whether an account was suspended based on these factors. Figure 1 visualizes the coefficients of a logistic regression model which predicts the binary suspension outcome based on BotHunter probabilities, hate speech scores, and offensive speech scores. Here, we see that accounts in the US dataset were more likely to be suspended if they had higher bot probabilities. Higher hate speech scores and higher offensive speech scores also predicted suspension in the US, but not as strongly for accounts that were also bot-like. Meanwhile, in the Philippines, higher suspension probability was observed for accounts that were bot-like, that expressed offensive speech, or that were both bot-like and expressing hate speech.

Tweet-level and user-level correlations

On both the basic tweet and user levels, we find no evidence of correlation between bot activity and levels of both hate speech and offensive speech. Pearson correlation coefficients estimated for all relationships are summarized in Table 3 , all indicating near-zero values with statistical significance. Thus, overall, tweets from more bot-like users were not necessarily more hateful or more offensive.

On balance, these initial results suggest that on a basic tweet or user level, clear conclusions are difficult to draw about the behavior of bots in relation to hate speech. In both the US and the Philippines, the large-scale public discourse triggered by the pandemic encompasses massive numbers of participants. Considering our findings, we observe that moments of crisis may trigger relatively common offensive or inappropriate content, but tweets by bots will not be the only ones expressing toxicity. Hence, we suggest the importance of accounting for the localized interactional settings in which both bot activity and hate speech occur. We tackle these questions in the succeeding sections.

Are hateful bots more influential?

Normalized mean centrality of users in the datasets. Users are divided into low, medium, and high hate scores, respectively defined as the bottom quartile, the middle 50%, and the top quartile of hate scores. Bot predictions are based on an 80% probability threshold

In the succeeding prose, we use the term high-hate bots to refer to users with BotHunter scores above our designated threshold of 80%, and hate scores in the highest quartile in our dataset. We assume that users with BotHunter scores with less than 80% are humans. Furthermore, we consider low-hate users based on the lowest quartile of hate speech scores; and mid-hate users in terms of the remaining two quartiles of the dataset, respectively. Through this approach, we identify low-hate bots and mid-hate bots, as well as humans with low-hate, mid-hate, and high-hate designations. We then examine their mean centrality scores to assess their relative influence in the dataset.

Figure 2 summarizes these results. For comparability, values are expressed relative to the maximum for each centrality score, which is linearly normalized to 1. Highlighting our social cybersecurity lens, we look at how different patterns of betweenness, closeness, eigenvector, and total degree centrality correspond to different relationships of bot activity and hate speech.

Bot hate and influence in the US

In the US, we find that low-hate bots have higher betweenness centrality than low-hate humans, but high-hate bots have lower betweenness centrality than low-hate bots. Hence, it appears that bots act more successfully as bridges when they express low levels of hate, but when they do express high levels of hate, they are more embedded within a single local neighborhood of the social network.

Closeness centrality suggests that the most well-distributed actors in the network—and, therefore, holding significant influence over network information flow—are low-hate humans and high-hate bots. This suggests two major faces of the US conversation about the pandemic, with one view controlled by organic, non-hateful users, and the other controlled by inorganic, hateful users.

Bots also consistently appear to have higher eigenvector centrality than their human counterparts, regardless of their level of hate. Thus, it seems that bots in the US conversation are interacting with other accounts which interact actively with others. These behaviors broadly align with positive network maneuvers under the BEND framework, whereby bots may seek to amplify their messages by swarming the interactions of network influencers.

Finally, we see that as hate increases, the total degree centrality of bots steadily decreases. Conversely, especially for high-hate humans, the total degree centrality of humans increases markedly. Hence, the most active accounts in propagating hate within Twitter interactions in the US are not actually bots, but humans.

Bot hate and influence in the Philippines

Meanwhile, in the Philippines, centrality measures feature consistently different patterns, suggesting a distinct information landscape of hate and bot activity. Overall, humans had higher betweenness centrality and total degree centrality than bots, regardless of their level of hate. The former result means that humans acted more as bridges between groups in the Philippine conversation, while bots were more likely to be focused on a single local neighborhood of the social network. The latter result, on the other hand, indicates that humans were also consistently the most engaged in all kinds of interactions with others.

That being said, we note that both betweenness centrality and total degree centrality also steadily increased as levels of hate increased. Hence, regardless of whether the account was a bot or a human, more hateful users served more powerful bridging functions between groups, and engaged in higher overall levels of Twitter interaction with others.

For closeness centrality, we observed only minor differences across the categories under observation. In general, however, bots tended to have higher closeness centrality than their human counterparts at comparable levels of hate. This indicates that information flow in the Philippine Twitter conversation around the pandemic tended to be controlled slightly more by inauthentic accounts, as these were more well-distributed throughout the social network.

A similar picture is seen in terms of eigenvector centrality. Bots also consistently had higher eigenvector centrality than their comparable human counterparts, indicating that they interacted with other influential others more often. But as hate increased, bots had lower eigenvector centrality; humans, on the other hand, had higher eigenvector centrality. This suggests that more hateful bots were less likely to interact with influencers in the conversation, but more hateful humans were more likely to do the same.

Whom do hateful bots target?

Mean identity score of users in dataset. Users are divided into low, medium, and high hate scores, respectively defined as the bottom quartile, the middle 50%, and the top quartile of hate scores. Bot predictions are based on an 80% probability threshold

In our third set of results, we further characterize bot–hate dynamics during the pandemic in terms of the identity groups that they target. Following the general understanding of hate speech as toxicity aimed toward specific groups, higher mentions of specific identity types among more bot-like and hateful users are suggestive of more consistent targeting. Our measurements are summarized in Fig. 3 . We use the same nomenclature to describe low-hate, mid-hate, and high-hate bots and humans. No normalization is conducted here, however, since all measures are on the same scale and order of magnitude. This additionally facilitates ordinal comparison of the most targeted identity groups.

As expected, for both datasets, more hateful users mention more identity terms across all categories. Based on a series of one-way ANOVAs, the results are consistent across gendered (US: $F = 13940, p < 0.001$ ; Philippines: $F = 103235, p < 0.001$ ), religious (US: $F = 2883, p < 0.001$ ; Philippines: $F = 74937, p < 0.001$ ), political (US: $F = 96901, p < .001$ ; Philippines: $F = 168914, t < 0.001$ ), and racial identities (US: $F = 368997, p < 0.001$ ; Philippines: $F = 56364, p < 0.001$ ). Generally, bots also follow the same ordinal trend with regard to which identity subclasses are targeted for both the US ( $r = 0.996, p < 0.001$ ) and the Philippines ( $r = 0.999, p < 0.001$ ). In other words, within the same country, bots and humans are discussing the same identity targets at similar levels.

In the US, the most salient identity category has to do with race and nationality. Less bot-like, yet high-hate accounts mention race and nationality identities more than their more bot-like counterparts. But among low-hate users, bots mention race and nationality more than humans. This may indicate that more hateful users are already engaged in racially charged discussion, while inauthentic accounts may seek to boost such discussions among non-hateful users.

Bots in the US are also more likely to mention political identities across the board, especially in more hateful messages. This points to more concerted attempts by inauthentic accounts to heighten politically charged conflicts amid the pandemic. For gendered and religious identities, we observe relatively low values for all categories, indicating that these may not be salient targets of conversation in either hateful or less hateful talk.

In the Philippines, on the other hand, political identities comprise the most prominently discussed group. Political identities featured especially in more hateful discussions, especially when users were more bot-like. Even among non-hateful users, however, political identities were likewise frequent targets of discussion, even more among humans than among bots. None of the other identity categories had particularly prominent discussion across more and less bot-like and hateful users. It is particularly noteworthy here that the salience of race and nationality is quite low, in comparison to its ubiquity in the US discussion.

Extending this comparative standpoint, it is also worth remarking that political identities—the most salient category in the Philippines—is discussed with identity scores in the 0.20–0.30 range for mid-hate and high-hate users. In contrast, identities associated with race and nationality are discussed in the US at a much higher rate with scores over 0.40 among high-hate users. This indicates that the difference in the targeting behavior of hate speech between countries is not just qualitative. It is also quantitative, with topics of race and nationality brought up over 50% more in the US than the most contentious political identities in the Philippines.

Does bot activity predict community hate?

We now consider whether there exist links between bot activity and hate speech on a community level. As shown in Sect. 4.2 , no strong relationship exists on a tweet level or a user level for bot activity and hate speech. Literature on the community-level dynamics of social media interactions, however, suggests that attention to localized relationships may unearth valuable insights for social cybersecurity [ 15 , 79 ].

Thus, we explore the relationship between hate speech and bot activity on the level of Leiden groups. Trivial clusters made up of isolates and pendants were removed to avoid analysis of degenerate groups. Given each social network for a given day, we note that Leiden grouping produced an average of 898 clusters in the US, with overall numbers ranging from 105 at the minimum to 2620 at the maximum. In the Philippines, Leiden grouping produced an average of 347.5 clusters, ranging overall from 44 to 1485 clusters on any given day in our dataset.

Accounting further for network maneuvers under the BEND framework, we set several cluster features described in Sect. 3.5 as additional predictors. We conduct this analysis over all Leiden groups derived for each time period, without accounting for temporal dynamics. These may be explored in subsequent work.

Table 4 summarizes the findings from our regression analysis. Community-level scores for hate and bot activity are computed as follows. We first let $h_{i,1}, h_{i,2}, \ldots , h_{i,n}$ be the predicted hate scores for each user in Leiden group i with $n_i$ accounts. Next, let $b_{i,1}, b_{i,2}, \ldots , b_{i,n}$ likewise represent their BotHunter scores. Then, the community-level hate score is given by $\bar{h_i} = \frac{1}{n}\sum _{j}h_{i,j}$ and the community-level bot score is given by $\bar{b_i} = \frac{1}{n}\sum _{j}b_{i,j}$ . Model 1 reports the base model without interaction effects. Model 2 includes all interactions between cluster features and community-level bot score. Model 3 optimizes the number of coefficients for parsimony by stepwise AIC backward selection. Results on both countries are reported separately.

Across both the US and the Philippines, we uncover a distinct finding: that higher community-level hate is predicted by the interaction of high community-level bot activity and high cluster density. These effects are significant in Model 2 for each country (US: $b = 0.065, {\text {SE}} = 0.019, p < 0.001$ ; Philippines: $= 0.169, {\text {SE}} = 0.031, p < 0.001$ ), as well as in Model 3 after AIC backward selection (US: $b = 0.065, {\text {SE}} = 0.019, p < .001$ ; Philippines: $b = 0.166, {\text {SE}} = 0.031, p < 0.001$ ). Overall, this suggests that bots spread hate most effectively in tightly connected groups.

Optimized in Model 3, the commonality of this finding across the two countries is especially important given the main effects of bot score (US: $b = -0.103, {\text {SE}} = 0.021, p < .001$ ; Philippines: $b = -0.041, {\text {SE}} = 0.007, p < 0.001$ ) and density (US: $b = -0.045, {\text {SE}} = 0.011, p < 0.001$ ; Philippines: $b = -0.091, {\text {SE}} = 0.018, p < 0.001$ ) alone. These estimates suggest that higher levels of bot activity in a non-dense community may not be associated with high hate scores; conversely, highly dense communities without much bot activity may also not be associated with more hate speech. Instead, it is the combined presence of high bot activity and frequent internal interactions predict greater levels of hate.

Furthermore, in the US, Model 3 shows that higher community-level hate is likewise predicted by the interplay of increased bot activity and decreased E/I indices ( $b = -\,0.074, {\text {SE}} = 0.022, p < 0.001$ ). This further underscores the community-level dynamics of bot-driven hate. Here, higher bot activity in clusters with low E/I indices suggests salient bot interactions in echo chamber-like groups. Bots in the US conversation thus spread hate in dense and isolated groups. We additionally note that these effects are derived controlling for cluster size and Cheeger scores. These respectively indicate that the observed effects were consistent across differently sized clusters; and that whether or not interactions were concentrated on a few influential accounts did not significantly dictate the relationship between bots and hate speech.

Finally, in the Philippines, Model 3 shows a main effect for E/I index ( $b = -\,0.042, {\text {SE}} = 0.008, p < 0.001$ ). This indicates that hate proliferates in the Philippines in echo chamber-like communities. This pattern is moreover irrespective of bot activity given that the backward selection process removes the interaction effect between bot scores and the E/I index entirely. A significant main effect was also detected for Cheeger scores ( $b = 0.064, {\text {SE}} = 0.025, p < 0.05$ ). Thus, bottlenecking behavior also predicts more hate; that is, in communities where discussion is dominated by a few users, hate is more likely to proliferate.

This study presented an empirical analysis of online hate speech in relation to bot activity in the context of the COVID-19 pandemic. Comparing case studies of the US and the Philippines, we found that bot-driven hate features idiosyncratic dynamics across contexts, but may also be linked to common sociotechnical processes. In the succeeding sections, we discuss the implications of these findings in relation to the broader literature, highlighting our contributions not only to extant scholarship on hate speech and disinformation, but also to computational social science more broadly.

Integrative approaches to online hate and disinformation

Prevailing approaches to online hate speech have focused on identifying it as a linguistic phenomenon [ 7 , 35 , 84 , 85 ]. In this work, we show how these methods can be reoriented toward the more situated task of characterizing hate speech in the concrete setting of understanding COVID-19 discourse [ 53 ]. Distinct studies have previously focused on the targeted nature of hate speech [ 2 , 32 , 33 ], its spread in communities [ 44 , 47 ], and the potential role of social bots [ 69 , 74 ]. Our work demonstrates a unified framework for viewing these phenomena as interlinked processes, thereby generating rich insights by examining their interplay.

In comparison to related studies, we found that our estimate of bot activity represented slightly lower levels than the proportions found by a related study on the COVID-19 pandemic, which predicted that 40.4% of all messages were bot-generated [ 36 ]. In a more extreme case, another study linked to spam activity surrounding financial microblogs posited an even higher proportion of around 71% bot activity [ 29 ]. However, our estimates do remain higher than the baseline reported in an earlier landmark study of bot activity, which estimates bot activity at around 9–15% [ 83 ]. Hence, our findings concur with the overall assessment that bot activity is higher during the pandemic relative to a general baseline. We surmise that the lower proportion may be due to the mainstream hashtags used in our data collection, whereas adjacent work on the pandemic has consciously analyzed hashtags with potentially incendiary implications to begin with, such as those which explicitly link the virus to China [ 89 ].

At the core of our findings, we underscore the embedded nature of online hate speech within large-scale interactional contexts and potential information maneuvers. In particular, we show that the relationship between bot activity and hate speech may not be straightforward on a tweet level, but must account for global as well as localized group structures. Comparing the US and Philippine cases, our measurement of network centrality metrics captured how hateful bots may orient toward distinct strategies or achieve varied levels of success in accruing influence and participating in the conversation more broadly. Context-specific distinctions notwithstanding, we emphasize two overarching insights gleaned from this analysis. First, bots—especially hateful bots—are influential and control major aspects of information flow in the pandemic conversation. Second, humans play a significant role in the spread of hate speech; recognition of organic participation in online toxicity will, thus, be vital for considering avenues for its mitigation [ 71 ].

Identity-based analysis of hate speech targets likewise showed the unique ways hate speech may be instrumentalized. Directing hate at particular racial or ethnic minorities—racial hate—may orient toward sowing conflict and deepening intergroup divisions. Meanwhile, directing hate at particular political leaders—political hate—may orient toward weakening trust in government institutions and extant leadership. Although united by a common overarching phenomenon, these two types of hate may also have distinct downstream real-world consequences. Recognizing the diverse nature of online hate, thus, helps to disentangle the distinct ways the pandemic exacerbates societal fractures. In the context of information operations, they also shed light on underlying strategic objectives which direct tactical maneuvers in cyber warfare [ 15 , 22 ].

But while the above results highlight nuanced, separate mechanisms characterizing online hate speech, we also uncovered common, potentially more universal processes. By paying attention to community level dynamics, we systematically showed shared conditions for the increase of hateful talk in public discourse. In line with extant theories of extreme communication, we specifically demonstrated the joint associations of more isolated communication and inauthentic bot activity with greater toxicity [ 8 , 64 , 70 ]. These insights also compellingly emphasize that narrative-based and network-based information maneuvers be studied in a complementary fashion, as manipulation of information flow in this case appears to relate strongly with shifting toxicity and aggression in the online conversation.

From a methodological standpoint, we finally remark on the wide variety of tools used to further understand online hate speech dynamics. More specifically, we show how machine learning, network science methods, as well as insights from social scientific theory can be used to better understand online hate speech as it spreads ‘in the wild’ [ 45 , 63 , 76 , 80 ]. Burgeoning literature on online disinformation and social cybersecurity advocates such integrated approaches to understanding sociotechnical problems like online hate speech [ 15 , 22 , 34 ]. This work illustrates how key principles of multidisciplinarity and interoperability may be applied in practice.

Toward global computational social science

The foregoing insights were facilitated specifically by adopting a global approach to computational social science [ 49 ]. By paying attention to multiple contexts, we situated our findings within a comparative frame, thereby allowing us to see which outcomes may be idiosyncratic to certain geopolitical settings, and which observed mechanisms may potentially be more general [ 79 ]. Furthermore, by situating online conversations within these geopolitical milieus, we were afforded more contextualized analysis.

For instance, observing higher levels of racial hate in the US, but not in the Philippines, made sense in relation to known societal conditions delineating the two nations [ 20 , 37 , 58 ]. While political polarization does indeed represent a serious problem for both nations [ 1 , 8 , 81 ], the multicultural setting of the US points to a fertile ground for racialized hate in a way that may not be comparable in the Philippines [ 5 , 28 , 44 , 89 ]. These findings also usefully show the localized boundaries of universally framed claims about the rise of racist discourse in relation to COVID-19 [ 73 ]. Our work, thus, usefully joins burgeoning scholarship aiming to extend beyond predominantly WEIRD populations in the social sciences, computational and otherwise [ 39 , 41 ]. As we hoped to demonstrate, this further strengthens our understanding of phenomena which are increasingly borderless yet may nonetheless feature unique dynamics within specific contexts.

Against the backdrop of the pandemic, both contexts face significant challenges at controlling outbreaks, and have extensively documented cases of online disinformation disrupting conversation in the public sphere [ 4 , 9 , 16 , 62 , 78 , 88 ]. Our findings point to crucial evidence of social conflicts exacerbated under these conditions, both organically as well as inorganically. As researchers seek to understand the pandemic’s impacts as an issue of both public health and societal importance more broadly, we suggest that these insights also matter for cultivating healthier digital ecosystems beyond the pandemic [ 15 , 22 , 55 ], as well as responding to genuine inequities which exist offline [ 10 , 31 , 51 , 54 ]

Limitations and future work

Finally, this research features several limitations which likewise point to potential future work. First, we consider that our reliance on machine learning algorithms assumes comparability in the distributions of quantities we train our models on and the quantities which emerge in the real-world setting we examine here. Although we used relatively general training data and achieved performant experimental accuracy [ 13 , 30 ], we note that COVID-19 represents an unprecedented phenomenon which may feature new and distinct patterns of public discourse. Thus, although we are confident in the broad patterns discerned in our analysis, individual predictions may feature errors unaccounted for given the novelty of the event being studied. Further work may aim to deepen the characterizations we offer here especially through qualitative assessment of predictions in relation to empirical observations. As models continue to evolve to adapt to dynamic phenomena, future methods may also be improved through attention to these idiosyncrasies.

Second, we point out that in line with our global approach, more drill-down analysis may be warranted to deepen our understanding of online hate and disinformation as complex sociotechnical phenomena. Our findings on both network influence and identity targets not only capture systematic tendencies in the data, but they also point to more complex social processes which our methods are not designed to capture. Highlighting the multidisciplinary nature of social cybersecurity and computational social science more broadly, our work points to promising avenues for future work in this area. More specifically, psychological processes of responding to hate in the community-level context of inauthentic bot activity [ 17 , 50 ] may be further probed in more controlled settings. Pivoting to the societal level of analysis, more macrosocial explanations regarding the differences in racially charged versus politically contentious hate in the US and the Philippines may also be explored [ 26 , 54 , 61 ].

Third, considering the large-scale nature of the global conversation about the pandemic, it may also be important for future work to consider the analysis shown here on more general datasets. Sampling in social media research can be fraught with generalizability issues [ 60 ]. Notwithstanding the limitations acknowledged in Sect. 3.1 , our strategies for data collection may also feature key limitations by constraining our search parameters with respect to the broader COVID-19 conversation. The availability of open datasets for the COVID-19 discourse on Twitter, however, mitigates these issues by providing standard benchmarks for researchers studying the same phenomenon more universally [ 24 ]. Thus, research which assumes a less narrow theoretical focus than ours may fruitfully extend our analysis to these larger-scale datasets.

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Acknowledgements

This work was supported in part by the Knight Foundation and the Office of Naval Research Grants N000141812106 and N000141812108. Additional support was provided by the Center for Computational Analysis of Social and Organizational Systems (CASOS) and the Center for Informed Democracy and Social Cybersecurity (IDeaS). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Knight Foundation, Office of Naval Research or the U.S. government.

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Uyheng, J., Carley, K.M. Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines. J Comput Soc Sc 3 , 445–468 (2020). https://doi.org/10.1007/s42001-020-00087-4

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School Year 2024-2025 kicks off with 19.8M students enrolled

Despite reeling from the effects of Typhoon Carina and the Southwest Monsoon (Habagat), the Department of Education (DepEd) has officially opened School Year 2024-2025, with students, along with their parents and guardians, flocking to schools early Monday morning.

Based on DepEd’s enrollment data as of 7:30 a.m. on Monday, there were 19,870,531 students enrolled so far in both public and private schools, including state and local universities and colleges (SUCs/LUCs), and the Alternative Learning System (ALS).

Majority of students in the country were still enrolled in public schools at 17.3 million.

Most of the enrollees came from Calabarzon at 2.8 million, followed by Central Luzon with 2.2 million and the National Capital Region with 2.1 million.

The least number of enrollees was recorded in the Cordillera Administrative Region at 242,437.

Thousands of schools, however, were affected by the inclement weather, resulting in some 979 opting to postpone the opening of classes.

As of Sunday, DepEd said classes in 452 schools in Central Luzon, 231 in Ilocos Region, 225 in NCR, 67 in Calabarzon, and four in Soccsksargen are suspended Monday.

President Ferdinand "Bongbong" Marcos Jr. said on Friday that the July 29 opening of classes must proceed as much as possible, save for areas heavily damaged by Carina and the Southwest Monsoon.

READ : Class suspensions for Monday, July 29, 2024

Marcos said the DepEd, led by the newly appointed former Senator Sonny Angara, should be able to figure out the middle ground since schools have not returned to full-time, face-to-face classes since the COVID-19 pandemic.

“May nasira na [mga] gamit, and we'd have to replace them. So yes, as much as possible, it will be up to the school to decide, kung kaya [nila magbukas] o hindi. Siguro ‘yung iba, mapipilitan. They will [have to] conduct classes outside of the school building, makapag klase lang,” he added.

(We'll have to replace certain school facilities, but there will be schools that will have to hold classes outside of classrooms.)

“Tingnan natin. Nasanay na tayo sa pandemic [na dapat] hahanapan natin ng paraan para magkaroon pa rin ng klase. Although there are areas na hindi talaga puwede,” Marcos added.

(We'll see. This is what happened during the pandemic: we try to look for ways to conduct classes regardless of the circumstance. But there are areas where school openings, as scheduled, are not possible.)

The Philippine National Police meanwhile said it has designated more than 33,000 personnel for the security of the opening of classes.

“In anticipation of the opening of classes today, the PNP has announced its comprehensive readiness to ensure the safety of students, teachers, and parents nationwide,” the PNP said in a statement.

“The PNP will deploy a total of 33,286 personnel to various schools throughout the country to ensure an orderly start to the academic year.”

The PNP said police assistance desks (PADs) will respond to safety concerns and will give immediate assistance in schools, and foot and mobile patrols will be done around campus premises to prevent crimes as well as ensure swift actions to possible incidents.

It said it will also extend assistance to schools that postponed their class opening due to the damage caused by the Southwest Monsoon or Habagat and Carina last week.

This school year also marks the start of the gradual return to the old school calendar from June until March.

As SY 2024-2025 is scheduled from July 29, 2024 to April 15, 2025 , the next academic year is set to begin on June 16, 2025, and end on March 31, 2026. —KG, GMA Integrated News

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Philippines Returns to School, Ending One of World’s Longest Shutdowns

Successful COVID Campaign in Philippines Wins Accolades

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Mental health and well-being of children in the Philippine setting during the COVID-19 pandemic

Maria Beatriz C. Baron

Hannah Andrea A. Sagsagat

Pamela Bianca M. Pasco

Roland Joseph D. Tan

Angelica Joyce Gacutno-Evardone

Don Eliseo Lucero-Prisno III

Introduction

Status of mental health system for children in the Philippines

General mental health implications of COVID-19 on Filipino children

Militarization of the Philippine COVID-19 response

Typhoons and the mental health of Filipino children

Child Labor and Abuse in the Time of COVID-19

Strategies Addressing the mental health implications of COVID-19 on Filipino children

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