理解网络错误信息背后的人类：透过 COVID-19 大流行视角开展的观察性研究
Understanding the Humans Behind Online Misinformation: An Observational Study Through the Lens of the COVID-19 Pandemic
AI总结：
本文通过分析数千万条 COVID-19 及历史推文，结合时间序列与因果推断，揭示用户跨领域传播错误信息的历史倾向与当前行为正相关。
摘要：
网络错误信息的扩散已成为社会面临的最大威胁之一。大量努力集中于构建错误信息检测模型，但错误信息的危害仍然普遍存在。缓解网络错误信息及其后果需要一种整体性方法，不仅要理解其相对于复杂议题和在线话题丰富的信息生态系统的错综复杂格局，还要理解其背后个体的心理驱动因素。我们采用时间序列分析技术和稳健的基于因果推断的设计，开展了一项大规模观察性研究，分析了超过 3200 万条 COVID-19 推文和 1600 万条历史时间线推文。我们重点理解 COVID-19 期间传播错误信息的用户的行为和心理，及其与大流行前在非 COVID 领域分享错误信息的历史倾向之间的关系。我们的分析强调了跨领域错误信息固有的复杂性，并指出用户分享错误信息的历史倾向与其当前在新兴话题及其他方面分享错误信息的行为呈正相关。本工作可为设计以用户为中心的接种策略和基于生态的敏捷干预措施、以有效应对网络错误信息提供宝贵基础。
英文摘要：
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 domains before the pandemic. Our analysis underscores the intricacies inherent to cross-domain 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
