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1. The Social Amplification and Attenuation of COVID-19 Risk Perception Shaping Mask Wearing Behavior: A Longitudinal Twitter Analysis

   Suellen Hopfer, Emilia Julia (January 2021, PloS one)

   Introduction Twitter represents a mainstream news source for the American public, offering a valuable vehicle for learning how citizens make sense of pandemic health threats like Covid-19. Masking as a risk mitigation measure became controversial in the US. The social amplification risk framework offers insight into how a risk event interacts with psychological, social, institutional, and cultural communication processes to shape Covid-19 risk perception. Methods Qualitative content analysis was conducted on 7,024 mask tweets reflecting 6,286 users between January 24 and July 7, 2020, to identify how citizens expressed Covid-19 risk perception over time. Descriptive statistics were computed for (a) proportion of tweets using hyperlinks, (b) mentions, (c) hashtags, (d) questions, and (e) location. Results Six themes emerged regarding how mask tweets amplified and attenuated Covid-19 risk: (a) severity perceptions (18.0%) steadily increased across 5 months; (b) mask effectiveness debates (10.7%) persisted; (c) who is at risk (26.4%) peaked in April and May 2020; (d) mask guidelines (15.6%) peaked April 3, 2020, with federal guidelines; (e) political legitimizing of Covid-19 risk (18.3%) steadily increased; and (f) mask behavior of others (31.6%) composed the largest discussion category and increased over time. Of tweets, 45% contained a hyperlink, 40% contained mentions, 33% contained hashtags, and 16.5% were expressed as a question. Conclusions Users ascribed many meanings to mask wearing in the social media information environment revealing that COVID-19 risk was expressed in a more expanded range than objective risk. The simultaneous amplification and attenuation of COVID-19 risk perception on social media complicates public health messaging about mask wearing. 

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2. The Social Amplification and Attenuation of COVID-19 Risk Perception Shaping Mask Wearing Behavior: A Longitudinal Twitter Analysis

   Suellen Hopfer, Emilia Julia (January 2021, PloS one)

   Article Authors Metrics Comments Media Coverage Peer Review Abstract Introduction Methods Results Discussion Conclusions Supporting information References Reader Comments Figures Abstract Introduction Twitter represents a mainstream news source for the American public, offering a valuable vehicle for learning how citizens make sense of pandemic health threats like Covid-19. Masking as a risk mitigation measure became controversial in the US. The social amplification risk framework offers insight into how a risk event interacts with psychological, social, institutional, and cultural communication processes to shape Covid-19 risk perception. Methods Qualitative content analysis was conducted on 7,024 mask tweets reflecting 6,286 users between January 24 and July 7, 2020, to identify how citizens expressed Covid-19 risk perception over time. Descriptive statistics were computed for (a) proportion of tweets using hyperlinks, (b) mentions, (c) hashtags, (d) questions, and (e) location. Results Six themes emerged regarding how mask tweets amplified and attenuated Covid-19 risk: (a) severity perceptions (18.0%) steadily increased across 5 months; (b) mask effectiveness debates (10.7%) persisted; (c) who is at risk (26.4%) peaked in April and May 2020; (d) mask guidelines (15.6%) peaked April 3, 2020, with federal guidelines; (e) political legitimizing of Covid-19 risk (18.3%) steadily increased; and (f) mask behavior of others (31.6%) composed the largest discussion category and increased over time. Of tweets, 45% contained a hyperlink, 40% contained mentions, 33% contained hashtags, and 16.5% were expressed as a question. Conclusions Users ascribed many meanings to mask wearing in the social media information environment revealing that COVID-19 risk was expressed in a more expanded range than objective risk. The simultaneous amplification and attenuation of COVID-19 risk perception on social media complicates public health messaging about mask wearing. 

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3. Data-driven inferences of agency-level risk and response communication on COVID-19 through social media-based interactions

   https://doi.org/10.5055/jem.0589  

   Ahmed, PhD Candidate; Sadri, PhD; Amini, PhD (October 2021, Journal of Emergency Management)

   Risk perception and risk averting behaviors of public agencies in the emergence and spread of COVID-19 can be retrieved through online social media (Twitter), and such interactions can be echoed in other information outlets. This study collected time-sensitive online social media data and analyzed patterns of health risk communication of public health and emergency agencies in the emergence and spread of novel coronavirus using data-driven methods. The major focus is toward understanding how policy-making agencies communicate risk and response information through social media during a pandemic and influence community response—ie, timing of lockdown, timing of reopening, etc.—and disease outbreak indicators—ie, number of confirmed cases and number of deaths. Twitter data of six major public organizations (1,000-4,500 tweets per organization) are collected from February 21, 2020 to June 6, 2020. Several machine learning algorithms, including dynamic topic model and sentiment analysis, are applied over time to identify the topic dynamics over the specific timeline of the pandemic. Organizations emphasized on various topics—eg, importance of wearing face mask, home quarantine, understanding the symptoms, social distancing and contact tracing, emerging community transmission, lack of personal protective equipment, COVID-19 testing and medical supplies, effect of tobacco, pandemic stress management, increasing hospitalization rate, upcoming hurricane season, use of convalescent plasma for COVID-19 treatment, maintaining hygiene, and the role of healthcare podcast in different timeline. The findings can benefit emergency management, policymakers, and public health agencies to identify targeted information dissemination policies for public with diverse needs based on how local, federal, and international agencies reacted to COVID-19. 

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4. #WashTheHate: Understanding the Prevalence of Anti-Asian Prejudice on Twitter During the COVID-19 Pandemic

   Wheeler, B.; Jung, S.; Nardini-Barioni, M. C.; Purohit, M.; Hall, D. L.; Silva, Y. N. (January 2022, The 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM))

   Prejudice and hate directed toward Asian individuals has increased in prevalence and salience during the COVID-19 pandemic, with notable rises in physical violence. Concurrently, as many governments enacted stay-at-home mandates, the spread of anti-Asian content increased in online spaces, including social media. In the present study, we investigated temporal and geographical patterns in social media content relevant to anti-Asian prejudice during the COVID-19 pandemic. Using the Twitter Data Collection API, we queried over 13 million tweets posted between January 30, 2020, and April 30, 2021, for both negative (e.g., #kungflu) and positive (e.g., #stopAAPIhate) hashtags and keywords related to anti-Asian prejudice. In a series of descriptive analyses, we found differences in the frequency of negative and positive keywords based on geographic location. Using burst detection, we also identified distinct increases in negative and positive content in relation to key political tweets and events. These largely exploratory analyses shed light on the role of social media in the expression and proliferation of prejudice as well as positive responses online. 

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5. Evolving Face Mask Guidance During a Pandemic and Potential Harm to Public Perception: Infodemiology Study of Sentiment and Emotion on Twitter

   https://doi.org/10.2196/40706  

   Ramjee, Divya; Pollack, Catherine C; Charpignon, Marie-Laure; Gupta, Shagun; Rivera, Jessica Malaty; El_Hayek, Ghinwa; Dunn, Adam G; Desai, Angel N; Majumder, Maimuna S (January 2023, Journal of Medical Internet Research)

   BackgroundThroughout the COVID-19 pandemic, US Centers for Disease Control and Prevention policies on face mask use fluctuated. Understanding how public health communications evolve around key policy decisions may inform future decisions on preventative measures by aiding the design of communication strategies (eg, wording, timing, and channel) that ensure rapid dissemination and maximize both widespread adoption and sustained adherence. ObjectiveWe aimed to assess how sentiment on masks evolved surrounding 2 changes to mask guidelines: (1) the recommendation for mask use on April 3, 2020, and (2) the relaxation of mask use on May 13, 2021. MethodsWe applied an interrupted time series method to US Twitter data surrounding each guideline change. Outcomes were changes in the (1) proportion of positive, negative, and neutral tweets and (2) number of words within a tweet tagged with a given emotion (eg, trust). Results were compared to COVID-19 Twitter data without mask keywords for the same period. ResultsThere were fewer neutral mask-related tweets in 2020 (β=–3.94 percentage points, 95% CI –4.68 to –3.21; P<.001) and 2021 (β=–8.74, 95% CI –9.31 to –8.17; P<.001). Following the April 3 recommendation (β=.51, 95% CI .43-.59; P<.001) and May 13 relaxation (β=3.43, 95% CI 1.61-5.26; P<.001), the percent of negative mask-related tweets increased. The quantity of trust-related terms decreased following the policy change on April 3 (β=–.004, 95% CI –.004 to –.003; P<.001) and May 13 (β=–.001, 95% CI –.002 to 0; P=.008). ConclusionsThe US Twitter population responded negatively and with less trust following guideline shifts related to masking, regardless of whether the guidelines recommended or relaxed mask usage. Federal agencies should ensure that changes in public health recommendations are communicated concisely and rapidly. 

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