Though 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 {\it 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 samples in the US, we distinguish different types of users, ranging from social bots to humans with various level 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 mechanism to mitigate the misinformation dissipation.
翻译:尽管已经加大了消除虚假主张和促进可靠来源等重大努力,以打击COVID-19“错误信息”,但如果缺乏对容易受访问的在线用户,即可能受到COVID相关信息吸引、相信和传播错误信息的人的适当理解,则仍是一个尚未解决的社会挑战。这项研究试图回答哪些人构成易受该大流行病在线错误信息影响的群体,以及哪些人具有很强的特征和短期行为信号,区分易受影响的用户和其他用户。我们的分析表明,在Twitter上收集了一个为期6个月的长距离用户面板,从美国地理上多样化的网络批准样本中收集,我们区分了不同的用户类型,从社会机器到与人之间接触程度不同的在线用户,即那些可能受到COVID-19错误信息吸引、相信的在线特征和情况预测因素。这项工作带来了独特的贡献:首先,与先前的肉毒影响研究相反,我们的分析表明,社会机器对共享错误信息的可行性低得令人惊讶,而像人类一样的用户的错误行为表明,在设计平均性和时间变异性方面,我们区分了不同的不同种类,从社会机器的社交网络和动态用户之间的敏感度,与此同时,他们之间也非常集中地分享了他们之间的准确性结论性结论性结论性结论性结论性结论,我们之间的不易变。