During the COVID-19 pandemic, when individuals were confronted with social distancing, social media served as a significant platform for expressing feelings and seeking emotional support. However, a group of automated actors known as social bots have been found to coexist with human users in discussions regarding the coronavirus crisis, which may pose threats to public health. To figure out how these actors distorted public opinion and sentiment expressions in the outbreak, this study selected three critical timepoints in the development of the pandemic and conducted a topic-based sentiment analysis for bot-generated and human-generated tweets. The findings show that suspected social bots contributed to as much as 9.27% of COVID-19 discussions on Twitter. Social bots and humans shared a similar trend on sentiment polarity—positive or negative—for almost all topics. For the most negative topics, social bots were even more negative than humans. Their sentiment expressions were weaker than those of humans for most topics, except for COVID-19 in the US and the healthcare system. In most cases, social bots were more likely to actively amplify humans’ emotions, rather than to trigger humans’ amplification. In discussions of COVID-19 in the US, social bots managed to trigger bot-to-human anger transmission. Although these automated accounts expressed more sadness towards health risks, they failed to pass sadness to humans.

BACKGROUND The rapid global spread of COVID-19 has become a monumental public health emergency. Gauging people’s psychological and behavioral reactions in an initial alerting stage is crucial for helping public health authorities to manage the epidemic. OBJECTIVE To investigate how spatial distance from the epicenter of Wuhan influenced people’s risk perceptions regarding COVID-19. Additionally, how risk perceptions, in concert with demographic variables, influenced the adoption of different preventive behaviors in the early stages of the outbreak. METHODS We conducted a national cross-sectional survey from January 21, 2020 to January 23, 2020. We assessed the association between spatial distance from the epicenter and participants’ risk perceptions using linear regression models. We used binomial logistic regression models to calculate the determinants of the adoption of six preventive behaviors against COVID-19. RESULTS Our data contain 1988 valid responses from 31 provinces in mainland China; 28.2% of respondants resided in Hubei province (n=560). Participant locations were roughly coded into five categories based on their geographical distance from the epicenter. We found that the closer people were to the initial epicenter in Wuhan, the higher susceptibility they felt (β=-.24, t=-11.12, P<.001), while their perceived severity displayed no significant variation based on location (β=-.02, t=-.93, P=.35). Compared with those in the peripheral provinces, people in Hubei and the forth-category provinces reported higher odds of wearing facemasks when going out (odds ratio [OR] 2.635 95%CI 1.33-4.17, P<.001; OR 3.19, 95%CI 1.78-5.72, P<.001, respectively). Participants with higher perceived susceptibility had a higher likelihood of wearing masks (OR 1.15, 95%CI 1.01-1.31, P=.04), however, lower odds of avoiding social gatherings (OR 0.87, 95%CI 0.77-0.99, P=.03) and avoiding visiting Wuhan (OR 0.69, 95%CI 0.61-0.77, P<.001). Participants’ perceived severity was positively associated with their engagement in washing hands and frequent ventilation (OR 1.12, 95%CI 1.00-1.24, P=.05), wearing masks in public (OR 1.39, 95%CI 1.25-1.55, P<.001), avoiding social gathering (OR 1.25, 95%CI 1.12-1.38, P<.001) and avoiding traveling to Wuhan (OR 1.13, 95%CI 1.02-1.25, P=.02). Participants’ sex was also associated with their perceived severity and the engagement of precautionary behaviors. CONCLUSIONS These results characterize an “epicenter effect” early in the pandemic. Our findings expand the understanding of perceived susceptibility and severity, which acted as two distinct dimensions of risk perception, and led to different behavioral outcomes.