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Social media platforms have numerous potential benefits and drawbacks on public health, which have been described in the literature. The COVID-19 pandemic has exposed our limited knowledge regarding the potential health impact of these platforms, which have been detrimental to public health responses in many regions.
This review aims to highlight a brief history of social media in health care and report its potential negative and positive public health impacts, which have been characterized in the literature.
We searched electronic bibliographic databases including PubMed, including Medline and Institute of Electrical and Electronics Engineers Xplore, from December 10, 2015, to December 10, 2020. We screened the title and abstracts and selected relevant reports for review of full text and reference lists. These were analyzed thematically and consolidated into applications of social media platforms for public health.
The positive and negative impact of social media platforms on public health are catalogued on the basis of recent research in this report. These findings are discussed in the context of improving future public health responses and incorporating other emerging digital technology domains such as artificial intelligence. However, there is a need for more research with pragmatic methodology that evaluates the impact of specific digital interventions to inform future health policy.
Recent research has highlighted the potential negative impact of social media platforms on population health, as well as potentially useful applications for public health communication, monitoring, and predictions. More research is needed to objectively investigate measures to mitigate against its negative impact while harnessing effective applications for the benefit of public health.
Humans are an inherently social species, and the evolutionary and health benefits of this trait are well documented [
Social media platforms have drawn criticism for propagating misinformation and crowding out of public health communication [
We searched electronic bibliographic databases, including PubMed, including Medline and Institute of Electrical and Electronics Engineers Xplore, from December 10, 2015, to December 10, 2020, with the following search terms: “((Social media[Title/Abstract]) OR (Social network[Title/Abstract]) OR (TikTok[Title/Abstract]) OR (Facebook[Title/Abstract]) OR (Instagram[Title/Abstract]) OR (Twitter[Title/Abstract]) OR (Baidu[Title/Abstract]) OR (Weibo[Title/Abstract])) AND ((Public health[Title/Abstract]) OR (Infectious Disease[Title/Abstract]) OR (Outbreak[Title/Abstract]) OR (Pandemic[Title/Abstract]) OR (COVID[Title/Abstract])) AND ((Intervention[Title/Abstract]) OR (Content analysis[Title/Abstract]) OR (Trial [Title/Abstract]) OR (Application[Title/Abstract]) OR (Health Promotion [Title/Abstract])) AND (English[Language]).”
A total of 678 reports were identified. We screened the title and abstract of these reports to identify relevant English-language manuscripts for this review. The full text of selected manuscripts and their reference lists were analyzed thematically on the basis of the thematic paradigm of social media apps for public health communication, monitoring, and predictions. This analysis was conducted by a multidisciplinary panel of clinicians, researchers, public health specialists, and professors from business and medical schools to provide a holistic assessment of the published literature. The findings of this panel based on the reviewed studies are described using a narrative review approach in accordance with specific issues described in the literature, which have a positive or negative impact on population health, in order to inform future public health responses.
Prior to the COVID-19 pandemic, the possibilities of scalable public health promotion through leveraging the network effects of social media had garnered praise from the academic community. The effectiveness of these platforms for dissemination of information, conduct of digital interventions, or individual campaigns can be evaluated at three levels of chronology. These include the short-term using level of engagement (frequency or duration a platform is accessed each day, number of reactions or shares to content, etc), medium-term with frequency of engagement (daily or monthly active users, etc), and long-term based on retention or duration of engagement (adherence to or compliance with digital interventions) [
Evaluating the impact and applications of social media in public health. AI: artificial intelligence, EHR: electronic health record, ILI: influenza-like illness.
The exponential potential of social media platforms for information dissemination has been strategically used for positive impact in the past [
The Tweet2Quit initiative attracted considerable attention to public health promotion using social media following a randomized controlled trial of a digital intervention using Twitter to help smokers abstain from their habit. It recruited users into twitter groups of 17-20 participants and encouraged smoking cessation by seeding conversation topics for users using automated messages to each group. These messages were aligned with clinical practice smoking cessation guidelines. The messages served as a conversation starter for users to provide encouragement for others, forging camaraderie as they embarked on their arduous smoking cessation journeys. The digital intervention was found to be more effective than Nicotine patches and a quit smoking website in this study [
The epidemiological value of social media applications includes surveillance of information, disease syndromes, and events (outbreak tracing, needs or shortages during disasters) [
During the initial onset of an outbreak, uncertainty promotes fear among members of the public, who become desperate for more information. During the H1N1 outbreak: internet attention peaked in the first 3 days before dwindling as information saturation set in [
During the 2009 H1N1 outbreak, real-time monitoring using Twitter was clearly demonstrated [
Infodemiology (ie, information epidemiology) entails methods which analyze trends in web-based health data for applications, such as policy making [
Although social media has the potential for positive public health utility, it can also amplify poor quality content [
Similar associations between media consumption and mental health disorders have been reported during the COVID-19 pandemic and were worsened by poor quality of information dissemination among quarantined undergraduates in France [
Despite the negative impact of social media in propagating “infodemics,” it also provides a reservoir of UGC as individuals share a range of topics from emotions to symptoms [
The volumes of fear-driven information sharing at the beginning of the pandemic overwhelmed individuals and the capacity of regulators in many regions [
Fortunately, new methods using topical modeling and engagement metrics in social media were available to allay the concerns of the public and provide updated information to health care professionals. These leveraged application programming interfaces (APIs) of platforms such as Twitter or Weibo to identify trends in content sharing to inform public health communications [
Finally, social media has also been applied as a tool for grassroots health promotion initiatives [
Comprehensive surveillance is vital during infectious disease outbreaks to monitor compliance and effectiveness of measures such as social distancing [
This underscores the importance of investigating the relationship between web-based and offline behavior for translatable population health benefits. Digital data from social media platforms has also been used to detect predatory sellers, counterfeit health products, and unapproved products with questionable claims [
The AI and regression techniques applied for the abovementioned real-time monitoring applications were based on cross-sectional data that became available during the pandemic. However, increases in computational power and availability of large, longitudinal data sets have paved the way for applications of big data from social media for future outbreak forecasting among other predictions. Applications that predict the potential number of cases during the COVID-19 outbreak used social media search indexes (SMSI) for keywords such as “dry cough,” “fever,” “coronavirus,” and “pneumonia” on platforms such as Baidu, where a significant correlation between new COVID-19 cases and SMSI findings have been reported [
Researchers have even developed and demonstrated such capabilities during the COVID-19 pandemic to accurately predict the burden of incident cases 2 weeks ahead of official sources [
However, it is worth noting that the CDC’s prediction initiative COVID-19 forecast hub has indicated that methods using social media big data have underperformed traditional methods such as the Susceptible-Exposed-Infectious Removed (SEIR) model when applied for forecasting. Ultimately, further research is necessary to fine-tune these novel techniques, and researchers may find that applying social media apps together with existing traditional modeling paradigms such as the SEIR may yield superior results. Limitations of existing modeling approaches include their primary focus on human-human transmission, along with difficulties modelling environmental transmission from fomites as well as variations in transmissibility. The latter is particularly important in a new public health emergency with growing awareness over time and public health communication such as that to encourage the adoption of hygiene measures. Public health organizations may also consider funding this research for capacity building to evaluate how these tools can be applied to enhance resource allocation during future health crises.
The COVID-19 pandemic has exposed the public health risks of unchecked health information–sharing on social media. It has also highlighted the pivotal role of human behavior in epidemic risk, prevention, and control [
The literature regarding social media apps for public health communication before the COVID-19 pandemic highlighted that digital behavioral modifications can be less time-consuming and less costlier than traditional approaches implemented using offline channels, such as patient support groups [
Although similar applications of social media for communication were effectively applied to amplify public health messages during the COVID-19 pandemic, they were also used by some to perpetuate the spread of misinformation, thus marring its positive impact [
New trends in personal content creation are constantly emerging, such as video logging (“vlogging”) using platforms such as TikTok or modules of established platforms such as “Stories” in Instagram [
Nonetheless, various reports of these public health responses to the COVID-19 pandemic, which applied social media for positive impact signal a future in which these platforms can be used to address new public health threats. Social media data can be combined with other sources of publicly available and digital behavioral data to improve the accuracy of existing approaches for various public health applications. These include analysis of UGC in open social media platforms such as Twitter, as well as internet search trends in search engines using ML. This has been demonstrated for applications such as monitoring for influenza surveillance [
However, the effectiveness of applying these tools at a population level has yet to be formally evaluated [
Future research is needed to develop scalable methods to mitigate against the risks of “online closeness.” Fortunately, solutions such as provider-moderated OHCs have emerged as potential tools to counter web-based medical misinformation, with applications described in fields such as psychiatry [
The incorporation of these various technologies with social media platforms may eventually contribute to a “learning” digital public health system in future, that can scale up and improve existing methods for targeted communication, monitoring, and predictions [
Finally, strategies for implementing these tools in health care macrosystems will also need to be developed. Examples of these include implementation of these tools using the lighthouse and safety net operational models for remote monitoring solutions [
The pandemic has had a massive human toll and economic impact [
artificial intelligence
application programming interface
US Centers for Disease Control and Prevention
electronic medical record
Lee Kong Chian School of Medicine
machine learning
National University of Singapore
online health community
Susceptible-Exposed-Infectious Removed
social media search indexes
user-generated content
DVG reports equity investment in AskDr, Doctorbell (acquired by MaNaDr Mobile Health), VISRE, and Shyfts. A Chia reports equity investment in Bodhi Health Education. The remaining authors have no relevant financial declarations. A Chew and VK are medical students on research attachment with DVG. DVG is a senior lecturer (Medical Innovation) at the National University of Singapore (NUS), and physician leader (Telemedicine) in Raffles Medical Group (RMG). EKC and PR are actively practicing clinicians trained in Public Health. MR is a tutor of Academic English at the Center for English Language and Communication, NUS. A Chia is associate professor of Management and Organisation at the NUS Business School with joint appointment at the Yong Loo Lin School of Medicine, NUS. HS is dually accredited in General Practice and Public Health, has practiced extensively in Canada and the United Kingdom, and is presently appointed as a professor at Lee Kong Chian School of Medicine (LKCMedicine), Singapore. CKL is trained in Family Medicine and Public Health and has practiced extensively at the Asia and Mission Medical Clinic, and contributes to the development of health policies and infectious disease guidelines in Singapore. CKL is also appointed as an adjunct assistant professor at Duke-NUS and the Yong Loo Lin school of Medicine, NUS, and adjunct clinical instructor at LKCMedicine.