ABSTRACT
Widely spread health-related rumors may mislead the public, escalate social panic, compromise government credibility, and threaten public health. Social collaboration models that maximize the functions and advantages of various agents of socialization can be a promising way to control health-related rumors. Existing research on health-related rumors, however, is limited in studying how various agents collaborate with each other to debunk rumors. This study utilizes content analysis to code the text data of health-related rumor cases in China during the COVID-19 pandemic. The study found that socialized rumor-debunking models could be divided into the following five categories: the government-led model, the media-led model, the scientific community-led model, the rumor-debunking platform-led model, and the multi-agent collaborative model. In addition, since rumors in public health crises often involve different objects, rumor refutation requires various information sources; therefore, different rumor-debunking models apply. This study verifies the value of socialized collaborative rumor debunking, advocates and encourages the participation of multiple agents of socialization and provides guidance for establishing a collaborative rumor-debunking model, thereby promoting efficient rumor-debunking methods and improving the healthcare of society.
ABSTRACT
The COVID-19 outbreak triggered a massive spread of unverified news on social media and has become a source of rumors. This paper studies the impact of a virtual rumor control center (RCC) on Weibo user behavior. The collected COVID-19 breaking news stories were divided into positive, negative, and neutral categories, while the moderating effect model was used to analyze the influence of anti-rumor on user behavior (forwarding, liking, and commenting). Our research found that rumor refuting does not directly affect user behavior but does have an indirect moderating effect. Rumor refuting has a profound impact on user forwarding behavior in cases of positive and negative news. Specifically, when the epidemic becomes more serious, the role of rumor refuting becomes critical, and vice versa. Refuting rumors reduces user willingness to forward positive or negative news, with more impact on negative news. Time lag analysis shows a significant moderation of unverified news within 72 h of refuting rumors but indicated an apparent weakening trend over time. Furthermore, we discovered non-linear feature and counter-cyclical phenomena in the moderating effect of rumor refutation.
Subject(s)
COVID-19 , Social Media , Humans , Social NetworkingABSTRACT
Objectives: To trace the emergence and dissemination of the most prominent rumours about potential adverse effects of COVID-19 vaccines. Methods: We use a weekly Google Trends search to gather information about what alleged adverse events are being associated with COVID vaccines by the general population. We then use CrowdTangle and Factiva searches to examine how discussions about the five most prominent adverse events have spread through traditional media channels and Facebook. Results: Traditional mass media reporting remains crucial in both promoting and moderating discussions around alleged adverse events. While some cases illustrate that social media networks can synthesise and amplify rumours about adverse events, traditional media coverage remains crucial as a forum for exploring and debunking spurious claims. Conclusion: Traditional media stories still bear signficant responsibility as credibility markers for rumours about vaccine adverse events. Journalists should therefore be encouraged to be particularly earnest when reporting such stories, and the scientific community should aid journalists in this task by clearly responding to any rumours emerging online.