Misinformation has captured the interest of academia in recent years with several studies looking at the topic broadly with inconsistent results. In this research, we attempt to bridge the gap in the literature by examining the impacts of user-, time-, and content-based characteristics that affect the virality of real versus misinformation during a crisis event. Using a big data-driven approach, we collected over 42 million tweets during Hurricane Harvey and obtained 3589 original verified real or false tweets by cross-checking with fact-checking websites and a relevant federal agency. Our results show that virality is higher for misinformation, novel tweets, and tweets with negative sentiment or lower lexical density. In addition, we reveal the opposite impacts of sentiment on the virality of real news versus misinformation. We also find that tweets on the environment are less likely to go viral than the baseline religious news, while real social news tweets are more likely to go viral than misinformation on social news.

近年来，错误信息引起了学术界的兴趣，多项研究对该主题进行了广泛研究，但结果不一致。在这项研究中，我们试图通过检查基于用户、时间和内容的特征的影响来弥合文献中的差距，这些特征会影响危机事件期间真实信息与错误信息的病毒式传播。使用大数据驱动的方法，我们在飓风哈维期间收集了超过 4200 万条推文，通过与事实核查网站和相关联邦机构的交叉核对，获得了 3589 条经过验证的真实或虚假推文。我们的结果表明，错误信息、新颖的推文和带有负面情绪或词汇密度较低的推文的病毒式传播更高。此外，我们揭示了情绪对真实新闻与错误信息的病毒式传播的相反影响。