Background During the coronavirus disease (COVID-19) pandemic, engagement in preventive behaviors and getting tested for the virus play a crucial role in protecting people from contracting the new coronavirus. Objective This study aims to examine how internet use, risk awareness, and demographic characteristics are associated with engagement in preventative behaviors and testing during the COVID-19 pandemic in the United States. Methods A cross-sectional survey was conducted on Amazon Mechanical Turk from April 10, 2020, to April 14, 2020. Participants’ internet use (in terms of the extent of receiving information pertaining to COVID-19), risk awareness (whether any immediate family members, close friends or relatives, or people in local communities tested positive for COVID-19), demographics (sex, age, ethnicity, income, education level, marital status, and employment status), as well as their engagement in preventative behaviors and testing were assessed. Results Our data included 979 valid responses from the United States. Participants who received more COVID-19–related health information online reported more frequent effort to engage in all types of preventive behaviors: wearing a facemask in public (odds ratio [OR] 1.55, 95% CI 1.34-1.79, P<.001), washing hands (OR 1.58, 95% CI 1.35-1.85, P<.001), covering nose and mouth when sneezing and coughing (OR 1.78, 95% CI 1.52-2.10, P<.001), keeping social distance with others (OR 1.41, 95% CI 1.21-1.65, P<.001), staying home (OR 1.40, 95% CI 1.20-1.62, P<.001), avoiding using public transportation (OR 1.57, 95% CI 1.32-1.88, P<.001), and cleaning frequently used surfaces (OR 1.55, 95% CI 1.34-1.79, P<.001). Compared with participants who did not have positive cases in their social circles, those who had immediate family members (OR 1.48, 95% CI 8.28-26.44, P<.001) or close friends and relatives (OR 2.52, 95% CI 1.58-4.03, P<.001) who tested positive were more likely to get tested. Participants’ sex, age, ethnicity, marital status, and employment status were also associated with preventive behaviors and testing. Conclusions Our findings revealed that the extent of receiving COVID-19–related information online, risk awareness, and demographic characteristics including sex, ethnicity, age, marital status, and employment status are key factors associated with US residents’ engagement in various preventive behaviors and testing for COVID-19.

Background Coronavirus disease (COVID-19) has affected more than 200 countries and territories worldwide. This disease poses an extraordinary challenge for public health systems because screening and surveillance capacity is often severely limited, especially during the beginning of the outbreak; this can fuel the outbreak, as many patients can unknowingly infect other people. Objective The aim of this study was to collect and analyze posts related to COVID-19 on Weibo, a popular Twitter-like social media site in China. To our knowledge, this infoveillance study employs the largest, most comprehensive, and most fine-grained social media data to date to predict COVID-19 case counts in mainland China. Methods We built a Weibo user pool of 250 million people, approximately half the entire monthly active Weibo user population. Using a comprehensive list of 167 keywords, we retrieved and analyzed around 15 million COVID-19–related posts from our user pool from November 1, 2019 to March 31, 2020. We developed a machine learning classifier to identify “sick posts,” in which users report their own or other people’s symptoms and diagnoses related to COVID-19. Using officially reported case counts as the outcome, we then estimated the Granger causality of sick posts and other COVID-19 posts on daily case counts. For a subset of geotagged posts (3.10% of all retrieved posts), we also ran separate predictive models for Hubei province, the epicenter of the initial outbreak, and the rest of mainland China. Results We found that reports of symptoms and diagnosis of COVID-19 significantly predicted daily case counts up to 14 days ahead of official statistics, whereas other COVID-19 posts did not have similar predictive power. For the subset of geotagged posts, we found that the predictive pattern held true for both Hubei province and the rest of mainland China regardless of the unequal distribution of health care resources and the outbreak timeline. Conclusions Public social media data can be usefully harnessed to predict infection cases and inform timely responses. Researchers and disease control agencies should pay close attention to the social media infosphere regarding COVID-19. In addition to monitoring overall search and posting activities, leveraging machine learning approaches and theoretical understanding of information sharing behaviors is a promising approach to identify true disease signals and improve the effectiveness of infoveillance.

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This article extends our understanding of risk communication related to communal risk and risk information sharing. Building on research from risk communication, organizational behavior, and social psychology, it examines individual-, relation-, and community-level motivations to share information about a devastating plant disease. This disease can bring about substantial economic risk to everyone in a farming community. We tested our hypotheses using a national sample of U.S. tomato and potato growers (N = 452). Our findings show that growers were motivated to share information about a communal risk based on (a) individual-oriented concerns for economic costs, (b) relation-oriented concerns for reciprocation and the information recipient's trustworthiness, and (c) community-oriented concerns comprising a sense of shared responsibility and community cohesiveness.

This study examines the organizational dynamics of social media crowds, in particular, the influence of a crowd's emotional expression on its solidarity. To identify the relationship between emotions expressed and solidarity, marked by sustained participation in the crowd, the study uses tweets from a unique population of crowds-those tweeting about ongoing National Football League games. Observing this population permits the use of game results as quasi-random treatments on crowds, helping to reduce confounding factors. Results indicate that participation in these crowds is self-sustaining in the medium term (1 week) and can be stimulated or suppressed by emotional expression in a short term (1 hour), depending on the discrete emotion expressed. In particular, anger encourages participation while sadness discourages it. Positive emotions and anxiety have a more nuanced relationship with participation.