The proliferation of online misinformation has emerged as one of the biggest threats to society. Considerable efforts have focused on building misinformation detection models, still the perils of misinformation remain abound. Mitigating online misinformation and its ramifications requires a holistic approach that encompasses not only an understanding of its intricate landscape in relation to the complex issue and topic-rich information ecosystem online, but also the psychological drivers of individuals behind it. Adopting a time series analytic technique and robust causal inference-based design, we conduct a large-scale observational study analyzing over 32 million COVID-19 tweets and 16 million historical timeline tweets. We focus on understanding the behavior and psychology of users disseminating misinformation during COVID-19 and its relationship with the historical inclinations towards sharing misinformation on Non-COVID topics before the pandemic. Our analysis underscores the intricacies inherent to cross-topic misinformation, and highlights that users' historical inclination toward sharing misinformation is positively associated with their present behavior pertaining to misinformation sharing on emergent topics and beyond. This work may serve as a valuable foundation for designing user-centric inoculation strategies and ecologically-grounded agile interventions for effectively tackling online misinformation.

翻译：在线虚假信息的泛滥已成为社会面临的最大威胁之一。大量研究致力于构建虚假信息检测模型，然而虚假信息的危害依然普遍存在。要缓解在线虚假信息及其影响，需要采用整体性方法，不仅要理解其与复杂议题和主题丰富的在线信息生态系统之间的错综关系，还要洞悉其背后个体的心理驱动因素。本研究采用时间序列分析技术和基于稳健因果推断的设计，开展了一项大规模观察性研究，分析了超过3200万条COVID-19相关推文和1600万条历史时间线推文。我们聚焦于理解COVID-19期间传播虚假信息的用户行为和心理，及其与疫情前在非COVID话题上分享虚假信息的历史倾向之间的关系。我们的分析强调了跨话题虚假信息的内在复杂性，并指出用户历史分享虚假信息的倾向与其当前在突发话题及其他领域分享虚假信息的行为呈正相关。本研究可为设计以用户为中心的预防策略和基于生态的敏捷干预措施提供重要基础，从而有效应对在线虚假信息问题。