Characterizing User Susceptibility to COVID-19 Misinformation on Twitter
Characterizing User Susceptibility to COVID-19 Misinformation on Twitter
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DOI：
10.1609/icwsm.v16i1.19353
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
Xian Teng;Yu-Ru Lin;Wen-Ting Chung;Ang Li;Adriana Kovashka
中科院分区：
文献类型：
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作者：
Xian Teng;Yu-Ru Lin;Wen-Ting Chung;Ang Li;Adriana Kovashka
尽管已经加大了消除虚假声明和推广可靠来源等重大努力来对抗COVID-19错误信息流行病，但如果缺乏对易感在线用户的正确了解，即，容易被误导、轻信、传播的。本研究试图回答谁构成了流行病中易受网络错误信息影响的人群，以及区分易感用户和其他人的强大特征和短期行为信号是什么。使用从美国地缘政治多样化的网络分层样本中收集的Twitter上为期6个月的纵向用户面板，我们区分了不同类型的用户，从社交机器人到与COVID相关错误信息有不同参与程度的人类。然后，我们确定了用户的在线特征和与他们对COVID-19错误信息的易感性相关的情景预测因素。这项工作带来了独特的贡献：首先，与之前关于机器人影响的研究相反，我们的分析表明，社交机器人对错误信息共享的贡献非常低，并且类人用户的错误信息行为表现出异质性和时间变异性。虽然错误信息的分享高度集中，但普通用户偶尔分享错误信息的风险仍然高得惊人。其次，我们的研究结果突出了敏感用户对情绪化内容的政治敏感性、活跃性和反应性。第三，我们展示了一个可行的解决方案，有效地预测用户的短暂的易感性，仅仅基于他们的短期新闻消费和曝光，从他们的网络。本文的研究对设计有效的干预机制以减缓虚假信息的传播具有一定的启示意义。
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 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.