Battling COVID-19 Infodemic
Study 1. Characterizing user susceptibility to COVID-19 misinformation on social media
Although significant efforts such as removing false claims and promoting reliable sources have been increased to combat COVID-19 “misinfodemic”, it remains an unsolved societal challenge if lacking a proper understanding of susceptible online users, i.e., those who are likely to be attracted by, believe and spread misinformation. This study attempts to answer who constitutes the population vulnerable to the online misinformation in the pandemic, and what are the robust features and short-term behavior signals that distinguish susceptible users from others. Using a 6-month longitudinal user panel on Twitter collected from a geopolitically diverse network-stratified sample in the US, we distinguish different types of users, ranging from social bots to humans with various levels of engagement with COVID-related misinformation. We then identify users’ online features and situational predictors that correlate with their susceptibility to COVID-19 misinformation. This work brings unique contributions: First, contrary to the prior studies on bot influence, our analysis shows that social bots’ contribution to misinformation sharing was surprisingly low, and human-like users’ misinformation behaviors exhibit heterogeneity and temporal variability. While the sharing of misinformation was highly concentrated, the risk of occasionally sharing misinformation for average users remained alarmingly high. Second, our findings highlight the political sensitivity, activeness and responsiveness to emotionally-charged content among susceptible users. Third, we demonstrate a feasible solution to efficiently predict users’ transient susceptibility solely based on their short-term news consumption and exposure from their networks. Our work has an implication in designing effective intervention mechanisms to mitigate the misinformation dissipation.
See the paper accepted by ICWSM 2022.
Study 2. Assessing the offline risk of COVID-19 from online misinformation in the United States
There is a growing concern that COVID-19 misinformation on social media is a major threat to global public health. Prior surveys have shown that trust in false claims is associated with lower levels of self-reported risk perceptions and compliance with health guidelines. However, there is still a lack of large-scale empirical studies that connect the online misinformation and the actual offline behavior signals. Here we study the risk of social media misinformation on mobility reduction in the United States between early March and late June 2020. Our overarching RQ is to whether online misinformation exposure affects people’s compliance with social distancing offline and to what extent. Specifically we examine – does the effect vary by different stages of this pandemic, does the effect vary by different topics, does the effect vary by different political ideologies? We combined daily, county-/state-level data on misinformation engagement from over 242 million tweets, movement changes across different places (e.g., recreation, grocery, and workplaces), non-pharmaceutical interventions (NPIs), and COVID-19 confirmed cases and deaths, along with county-/state-level census data on population demographics and presidential election results. We are facing two major challenges. The first one is to deal with confounding variables that might simultaneously affect both treatment and outcome. For example, political orientation can be such a confounder, there is a gap of misinformation rate as well as a gap of willingness to stay-at-home among opposite partisanships. The second challenge is “network interference,” i.e., users in location A might see COVID misinformation that is published by users in location B. We develop techniques to solve those challenges and assess the causal influence of online misinformation on offline mobility behaviors.
The preliminary results have been presented at PaCSS 2022 conference. More results coming soon.