ABSTRACT
Infodemic is defined as 'an overabundance of information-some accurate and some not-that makes it hard for people to find trustworthy sources and reliable guidance when they need it' by the World Health Organization. As unverified information, rumors can widely spread in online society, further diffusing infodemic. Existed studies mainly focused on rumor detection and prediction from the statement itself and give the probability that it will evolve into a rumor in the future. However, the detection and prediction from rumors production perspective is lack. This research explores the production mechanism from the uncertainty perspective using the data from Weibo and public rumor data set. Specifically, we identify the public uncertainty through usergenerated content on social media based on systemic functional linguistics theory. Then we empirically verify the promoting effect of uncertainty on rumor production and constructed a model for rumor prediction. The fitting effect of the empirical model with the public uncertainty is significantly better than that with only control variables, indicating that our framework identifies public uncertainty well and uncertainty has a significantly predictive effect on rumors. Our study contributes to the research of rumor prediction and uncertainty identification, providing implications for healthy online social change in the post-epidemic era.
ABSTRACT
This study examines the influences of media exposure, social realism, and proxy efficacy on the perceived effects of news about the COVID-19 pandemic. Using data collected from a sample of 1,190 college students in China, this study found that exposure to news about the pandemic is positively related to social realism and proxy efficacy. Furthermore, social realism and proxy efficacy were significantly correlated with third-person perception (TPP). Most importantly, the results of the study show that social realism and proxy efficacy also mediated the relationship between exposure to COVID-19 news and TPP.