Public Health Messaging in an Era of Social Media
Public health organizations have always used messaging to educate the public in an attempt to control the spread of epidemic diseases. Early efforts that relied on word-of-mouth communication and poster campaigns transitioned to radio and television as those technologies emerged, yet these forms of communication likely have become less effective in a crowded, noisy, and confrontational online environment. Over the past decade, emerging digital platforms have become sophisticated, targeted, and responsive in reaching and influencing the public.
Widespread physical distancing during the coronavirus disease 2019 (COVID-19) pandemic has limited informal social interactions and exposure to signage is considerably lessened as many people limit travel, order products online, and work from home. An effective messaging strategy will require meeting people where they are and through the information networks and devices they use for day-to-day interactions. As of December 27, 2020, more than 80 million people have contracted COVID-19 and 1.7 million have died. The need for effective public health messaging about community spread, prevention measures, and vaccines is more important than ever.
Digital platforms are powerful yet underused tools for engaging the public and should be considered essential for public health preparedness, response, and recovery. This Viewpoint explores the following 4 strategies to advance public health messaging during this and future public health emergencies: deploying countermeasures for misinformation, surveillance of digital data to inform messaging, partnering with trusted messengers, and promoting equity through messaging.
Misinformation Is a Public Health Crisis
Misinformation is a serious threat to public health, especially during pandemics. Misinformation has likely accelerated the spread of COVID-19 by fragmenting and influencing the response to prevention strategies like wearing a mask and physical distancing. Misinformation has emerged about nearly every aspect of the pandemic, including the origins of the virus (eg, it was manufactured in a laboratory), treatments (eg, bleach, alcohol), and vaccine safety (eg, vaccines include embedded microchips).
Misinformation is very difficult to correct because it is massive in volume, contagious, and can appear to come from trusted social networks. Because misinformation is not labeled, distinguishing it from credible information can be increasingly difficult, particularly when highly politicized and opposing views are categorized as fake news. Furthermore, misinformation is being distributed from multiple sources, including government leaders in the US and reportedly from highly organized groups in Russia and other countries.
Recent research is informing the development of approaches to counteract misinformation. Prior studies support a “find and replace” approach that includes posting timely corrections about what is false and why and frequently reiterating accurate information.1 The vaccine hesitancy literature also suggests the importance of understanding the origin of rumors and false information.2 This approach can help to understand the concerns and ideologies that can inform development of thoughtful and responsive countermeasures. Because each social media platform has different features and users, the strategies that public health organizations use to address misinformation should also be distinct and responsive to the nuances of each site. A study of 8 million posts about COVID-19 on 5 social media platforms (Twitter, Instagram, YouTube, Reddit, and Gab) demonstrated variability in both the content of accurate and inaccurate information and ways in which it spread across each site.3 Sites varied in the volume of COVID-19–related messages about topics like cures, therapies, and protection advice; for example, Reddit limited the influence of questionable sources, whereas Gab increased the diffusion of questionable sources.
Public health organizations should also work with technology companies that have platforms with the infrastructure to label misinformation and limit its spread. In response to COVID-19, the World Health Organization partnered with several entities (eg, Facebook, Twitter) to address false information and promote health updates.
In the midst of this pandemic, there is an immediate need to evaluate the effectiveness of these and other countermeasures against misinformation. It will be difficult to make progress toward an unmeasured and understudied problem. Identifying misinformation as a public health crisis has the potential to bring the full weight of public health organizations and other entities toward addressing this problem.
Surveillance of Digital Data to Inform Public Health Messaging
Data from social media can provide insights about the response of the public to preventive health measures. For example, information from social media, digital platforms (eg, OpenTable, Google), and remote sensors is already being used to track the movement of populations and understand where individuals are adhering to guidelines about physical distancing and where they are not. A study of 580 million tweets posted during the early months (ie, January to May 2020) of the pandemic showed that the geographic information associated with tweets can be used as a proxy for human mobility.4 Public health organizations can use this real-time data to inform messaging content and where to target hyperlocal messages about public health measures (eg, shelter in place).
In addition to serving as a proxy for behavioral responses, social media data can also provide information about public opinion and sentiment in response to public health interventions. Traditional approaches for assessing public attitudes and well-being rely on phone or mail surveys. Delays in the availability of these types of data limit their use for timely surveillance in evolving crises. During the early phases of the COVID-19 pandemic, the Penn Medicine Center for Digital Health and the World Well-Being Project launched a public-facing platform that uses machine learning approaches to synthesize data from Twitter about COVID-19 in real time.5 Based on data from this platform and others and to enhance public awareness, the Washington State Department of Health posts weekly online behavioral health situation reports containing regional estimates of sentiment, loneliness, and anxiety.6 This approach could be further harnessed to support situational awareness and enable more directed health messaging to address well-being and population-level mental health needs in response to COVID-19.
Align With Trusted Messengers
Individuals often rely on information that is passed along from people they know and respect. Community organizers and political campaign strategists invest in identifying individuals embedded in communities to help spread important information to their friends and neighbors. Similarly, public health organizations should partner with community influencers (eg, community health workers, religious leaders) who can help with propagating trustworthy messages and dismantling false ones through online channels. In the case of vaccine campaigns, trusted messengers are often engaged to disseminate information about vaccine safety.2
Public health organizations can also learn from social movements such as #BlackLivesMatter and #MeToo in elevating the ideas and experiences of young and often marginalized groups to shape a national dialogue that leads to change. Frontline leaders for these movements are often trusted messengers that public health organizations can collaborate with when trying to access vulnerable communities. Messages crafted and disseminated by these influencers will likely reach more people and be more persuasive than those created solely by organizations. Furthermore, messages that speak to multiple facets of what people are experiencing (eg, COVID-19 and racism, anxiety, unemployment, caregiving, homeschooling, or isolation) and leverage narratives and storytelling are likely to be effective.
Equity and Public Health Messaging
To date, Black, Latino, and Native American communities are disproportionately affected by COVID-19 and its economic consequences. Conversations about equity are at the forefront of national consciousness as a result of the intersecting events of the COVID-19 pandemic and the killing of George Floyd and other Black individuals. Racism is a centuries-old construct that will not be dismantled with a series of hashtags and Instagram stories. However, social media is an important tool that public health organizations can use to address racism and equity with the same focus as the messages being deployed about COVID-19.
Inequalities contribute to how different groups access, process, and share information. Research from the H1N1 pandemic highlighted how differences in exposure to information and differences in modes of delivery could influence the response to the outbreak.7 Structural barriers should also be addressed. Disparities in access to broadband and Wi-Fi limit exposure and access to digital content for some populations. This has been of particular salience for online public school education during this pandemic and supports the need for equity in access to technology infrastructure as well as a diversity of communication approaches.
Attentiveness to Risks and a Need for Adaptability
There are notable risks (eg, violations of privacy, data security, perpetuation of bias) with engaging on social media that must be carefully considered for deploying health messaging online. Yet, not expanding traditional approaches and engaging with the full complement of available digital strategies represents a missed opportunity. Just as communication approaches have evolved over time to respond to each emerging public health emergency, there is now urgency for harnessing new approaches to effectively engage the public.
Corresponding Author: Raina M. Merchant, MD, MSHP, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104 (raina.merchant@pennmedicine.upenn.edu).
Published Online: January 4, 2021. doi:10.1001/jama.2020.24514
Conflict of Interest Disclosures: Dr Merchant is funded by grant R01HL141844 from the National Heart, Lung, and Blood Institute, grant R01HG009655 from the National Human Genome Research Institute, and grant R21 1DA050761 from the National Institute on Drug Abuse. Dr South is funded by award 76233 from the Robert Wood Johnson Foundation Harold Amos Medical Faculty Development Program. Dr Lurie is an employee of the Coalition for Epidemic Preparedness Innovation.
Additional Information: Dr Lurie served as Assistant Secretary for Preparedness and Response in the US Department of Health and Human Services from 2009 to 2017.
Public Mobility and Social Media Attention in Response to COVID-19 in Sweden and Denmark
To reduce the spread of coronavirus disease 2019 (COVID-19), countries have implemented different societal interventions, and evaluation of the effect on public response is needed.1 Sweden and Denmark are comparable countries in terms of health care and sociodemographic characteristics; however, Denmark was one of the first countries to enforce lockdown and subsequent gradual reopening, whereas Sweden has had few restrictions, largely limited to public recommendations. We assessed public mobility and social media attention associated with COVID-19 spread and societal interventions from February 15 to June 14, 2020, in Denmark and Sweden.
For this ecological study, public mobility was measured by Google mobility reports, providing daily percentage change in the number of visitors to public spaces compared with baseline (January 3 to February 6, 2020).2 We focused on mobility across retail and recreational spaces based on previous findings.3 Daily volume of tweets including COVID-19–related hashtags and keywords (eTable in the Supplement) were collected via Sprout Social to study public attention to the disease, capturing 732 634 tweets in Sweden and 324 730 in Denmark. Because this study used summary statistics rather than individual-level data, an institutional ethical review permit was not required according to the rules at Karolinska Institutet, Sweden. This study followed the part of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline that are relevant to ecological studies.
The exposures that we focused on were disease spread (ie, daily new COVID-19 cases)4 and key societal interventions and announcements (ie, addresses by the prime minister or head of state) targeting public mobility. Interrupted time series analyses were conducted to evaluate the association between societal interventions and mobility, including both a level (β1) and a slope change (β2) that can be interpreted as the modeled percentage change in mobility from before to after the intervention.5 Statistical analyses were performed using R, version 3.6.3 (R Project for Statistical Computing). Details on measures and interrupted time series analyses are provided in the eMethods in the Supplement.
The percentage of mobility change, the number of COVID-19–related tweets, and the number of confirmed cases of COVID-19 are plotted with key societal interventions in the Figure. Daily volumes of COVID-19–related tweets followed a similar pattern in Denmark and Sweden, with volumes increasing exponentially during the beginning of disease spread and peaking between March 13 and 17 (from 746 on February 14 to 16 851 on March 13 in Sweden and from 25 to 8398 in Denmark). During this period, the World Health Organization declared a COVID-19 pandemic, Denmark announced a lockdown, and Sweden banned public gatherings or more than 500 people and recommended remote higher education and work. Twitter volumes then decreased with time in both countries. Public mobility to retail and recreation decreased in both countries during the study period, with a greater reduction in Denmark during lockdown. The maximum mean (SD) mobility reduction (apart from public holidays) in Denmark was 38% (2.6%) (from March 23 to 29) and 24% (2.1%) in Sweden (from March 30 to April 5). By the last week of the study period (June 8 to 14), mobility had returned to baseline levels in both countries, with a mean (SD) change of 2.14% (4.4%) in Denmark and −0.14% (5.1%) in Sweden (Figure).
The interrupted time series analyses showed that the Danish lockdown was associated with a 20% immediate decrease in mobility (β1, –20.19; 95% CI, –27.79 to –12.59) (Table). The ban of gatherings of more than 10 people coincided with the Queen’s address to the nation and was associated with a 12% decrease in mobility (β1, –12.20; 95% CI, –19.74 to –4.65). Phase 1 and 2 of the Danish reopening were associated with immediate reductions in mobility but increased mobility in the subsequent days. In Sweden, only the ban on public gatherings of more than 500 people was associated with a significant decrease in mobility (β1,–11.04; 95% CI, –14.66 to –7.42). Neither the prime minister’s nor the King’s address to the nation showed a statistically significant association with mobility. In both countries, higher numbers of daily confirmed COVID-19 cases were associated with reduced mobility (Denmark: β1,–2.66 [95% CI, –4.51 to –0.82]; Sweden: β1, –0.62 [95% CI, –0.84 to –0.40]).
In this cross-country comparison, stricter interventions were associated with larger reductions in mobility. Bans on public gatherings showed stronger associations with mobility in both countries compared with national announcements by country leaders and public recommendations, which were the primary intervention types in Sweden. We also found a delay in reaching maximum reduction in mobility after restrictions and for normalization of mobility after relaxation of restrictions, in line with prior research.6 Social media attention to COVID-19 on Twitter was greater for early interventions and disease spread than for later interventions and disease spread, with nearly identical patterns of decreasing COVID-19–related Twitter volumes across Denmark and Sweden despite marked difference in number of cases and the type of societal interventions. Better understanding of how interventions are associated with public mobility and attention may guide policy in relation to the resurgence of COVID-19 currently observed in many countries. Our findings are limited to short-term effects of specific interventions and do not capture interactions between these nor other important factors likely to be associated with public mobility and attention (eg, global media, guidelines from international health care organizations, and treatment and vaccine developments). Any conclusions are limited by the representativeness of Twitter and Goggle mobility data.
Accepted for Publication: November 22, 2020.
Published: January 4, 2021. doi:10.1001/jamanetworkopen.2020.33478
Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Zhang L et al. JAMA Network Open.
Corresponding Author: Isabell Brikell, PhD, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12 A, 171 77 Stockholm, Sweden (isabell.brikell@ki.se).
Author Contributions: Drs Brikell and Zhang contributed equally to this work. Drs Brikell and Zhang had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: Brikell, Chang.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Brikell.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Zhang, Chang.
Obtained funding: Chang.
Administrative, technical, or material support: Zhang, Chang.
Supervision: Brikell, Dalsgaard, Chang.
Conflict of Interest Disclosures: Dr Brikell reported receiving grants from the Swedish Brain Foundation. Dr Dalsgaard reported receiving grants from The Lundbeck Foundation, the Novo Nordisk Foundation, the European Commission, Helsefonden, and the European Union’s Horizon 2020 research and innovation programme. Dr Chang reported receiving grants from the Swedish Research Council and the Swedish Research Council for Health, Working Life and Welfare. No other disclosures were reported.
