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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Sociocultural determinants of global mask-wearing behavior
Behavioral responses influence the trajectories of epidemics. During the COVID-19 pandemic, nonpharmaceutical interventions (NPIs) reduced pathogen transmission and mortality worldwide. However, despite the global pandemic threat, there was substantial cross-country variation in the adoption of protective behaviors that is not explained by disease prevalence alone. In particular, many countries show a pattern of slow initial mask adoption followed by sharp transitions to high acceptance rates. These patterns are characteristic of behaviors that depend on social norms or peer influence. We develop a game-theoretic model of mask wearing where the utility of wearing a mask depends on the perceived risk of infection, social norms, and mandates from formal institutions. In this model, increasing pathogen transmission or policy stringency can trigger social tipping points in collective mask wearing. We show that complex social dynamics can emerge from simple individual interactions and that sociocultural variables and local policies are important for recovering cross-country variation in the speed and breadth of mask adoption. These results have implications for public health policy and data collection.
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- Award ID(s):
- 1917819
- NSF-PAR ID:
- 10401724
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 119
- Issue:
- 41
- ISSN:
- 0027-8424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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What influences the adoption of SARS-CoV-2 mitigation behaviors–both personal, such as mask wearing and frequent handwashing, and social, such as avoiding large gatherings and physical contact–across countries? Understanding why some individuals are more willing to change their behavior to mitigate the spread of a pandemic will not only help us to address the current SARS-CoV-2 pandemic but also to respond to future ones. Researchers have pointed to a variety of factors that may influence individual adoption of personal and social mitigation behaviors, including social inequality, risk perception, personality traits, and government policies. While not denying the importance of these factors, we argue that the role of trust and confidence has received insufficient attention to date. Our study explores whether there is a difference in the way trust and confidence in particular leaders and organizations affect individual compliance and whether this effect is consistent across different types of mitigation behaviors. Specifically, we utilize an original cross-national survey conducted during the first wave of the SARS-CoV-2 pandemic (May-June 2020) to investigate how trust in scientists, medical professionals, politicians, and religious leaders and confidence in global, national, and local health organizations affects individual compliance in 16 countries/territories across five world regions. Our analyses, which control for the aforementioned factors as well as several others, suggest that trust in politicians and confidence in national health ministries have the most consistent influence on whether individuals adopt both personal and social mitigation behaviors. Across our sample, we find that greater trust in politicians is associated with lower levels of individual compliance with public health directives, whereas greater confidence in the national health ministry is associated with higher levels of individual compliance. Our findings suggest the need to understand trust and confidence as among the most important individual level characteristics driving compliance when developing and delivering messaging about the adoption of mitigation behaviors. The content of the message, it seems, will be most effective when citizens across countries trust its source. Trusted sources, such as politicians and the national health ministry, should thus consider working closely together when determining and communicating recommended health behaviors to avoid contradicting one another.
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We investigated how tightness-looseness, reflecting strictness of social norms, of state of residence in the USA predicts behaviors and attitudes related to COVID-19. Because individual-level tightness may better capture current attitudes during the pandemic, whereas state-level archival measures reflect historical factors, we assessed the extent to which tightness-looseness at both levels predicted adherence to public health guidelines and biases toward outgroups related to COVID-19. In Spring 2020, 544 mTurk participants, primarily from the 13 tightest and 13 loosest states, completed survey questions about health behaviors in response to COVID-19, endorsement of future policy changes, feeling of responsibility for lives, and attitudes toward groups marginalized during the pandemic (i.e., Asians, older adults). State-level results indicated some associations with attitudes toward Asians and older adults, but effects were not robust. Results based on individuals’ ratings of the tightness of their state indicated that higher levels of perceived tightness were associated with higher levels of protective self-reported public health behaviors (e.g., mask wearing, handwashing) during COVID-19, more endorsement of future policy changes to contain the pandemic, higher reported feelings of responsibility for one’s life, and stronger negative attitudes toward Asians. The relations between tightness and health outcomes persisted after controlling for political attitudes and demographics. Thus, individual, more than state, tightness-looseness accounted for some degree of public health behaviors (unique contribution of individual tightness: R 2 = .034) and attitudes toward marginalized groups ( R 2 = .020) early during the COVID-19 pandemic. The implications of these findings for interventions to support behavior change or combat anti-Asian bias are discussed.more » « less
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Eksin, Ceyhun (Ed.)The global pandemic of COVID-19 revealed the dynamic heterogeneity in how individuals respond to infection risks, government orders, and community-specific social norms. Here we demonstrate how both individual observation and social learning are likely to shape behavioral, and therefore epidemiological, dynamics over time. Efforts to delay and reduce infections can compromise their own success, especially when disease risk and social learning interact within sub-populations, as when people observe others who are (a) infected and/or (b) socially distancing to protect themselves from infection. Simulating socially-learning agents who observe effects of a contagious virus, our modelling results are consistent with with 2020 data on mask-wearing in the U.S. and also concur with general observations of cohort induced differences in reactions to public health recommendations. We show how shifting reliance on types of learning affect the course of an outbreak, and could therefore factor into policy-based interventions incorporating age-based cohort differences in response behavior.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1073/pnas.2213525119
