Monitoring Misinformation on Twitter During Crisis Events: A Machine Learning Approach
Monitoring Misinformation on Twitter During Crisis Events: A Machine Learning Approach
复制标题
DOI：
10.1111/risa.13634
复制
发表时间：
2020-11-14
期刊：
影响因子：
3.8
通讯作者：
Zhuang, Jun
中科院分区：
文献类型：
--
作者：
Hunt, Kyle;Agarwal, Puneet;Zhuang, Jun
在各种规模的灾害期间，社交媒体越来越多地用于传播突发新闻和风险通信。不幸的是，由于Twitter等社交媒体平台的无节制性质，谣言和错误信息能够广泛传播。有鉴于此，大量的研究已经研究了Twitter上的虚假谣言传播，特别是在自然灾害期间。在这一领域，研究还集中在政府组织和其他主要机构的错误信息控制工作。在研究灾害和其他危机事件发生时对社交媒体平台上错误信息的监测方面存在巨大的差距。这些研究将为组织和机构提供新的工具和意识形态，以监测Twitter等平台上的错误信息，并就是否使用其资源进行揭穿做出明智的决定。在这项工作中，我们通过开发一个机器学习框架来预测危机事件期间传播的推文的准确性，从而填补了研究空白。根据内容的真实性（真实、虚假或中性）来跟踪推文。我们进行了四项独立的研究，结果表明，我们的框架能够同时跟踪多个错误信息案例，F1得分超过87%。在跟踪单个错误信息的情况下，我们的框架达到了83%的F1分数。我们收集了15,952条与波士顿马拉松爆炸案（2013年）、曼彻斯特竞技场爆炸案（2017年）、哈维飓风（2017年）、厄玛飓风（2017年）和夏威夷弹道导弹错误警报（2018年）有关的错误信息，并以此来驱动算法。本文提供了关于如何有效监控灾难期间传播的错误信息的新见解。
Social media has been increasingly utilized to spread breaking news and risk communications during disasters of all magnitudes. Unfortunately, due to the unmoderated nature of social media platforms such as Twitter, rumors and misinformation are able to propagate widely. Given this, a surfeit of research has studied false rumor diffusion on Twitter, especially during natural disasters. Within this domain, studies have also focused on the misinformation control efforts from government organizations and other major agencies. A prodigious gap in research exists in studying the monitoring of misinformation on social media platforms in times of disasters and other crisis events. Such studies would offer organizations and agencies new tools and ideologies to monitor misinformation on platforms such as Twitter, and make informed decisions on whether or not to use their resources in order to debunk. In this work, we fill the research gap by developing a machine learning framework to predict the veracity of tweets that are spread during crisis events. The tweets are tracked based on the veracity of their content as either true, false, or neutral. We conduct four separate studies, and the results suggest that our framework is capable of tracking multiple cases of misinformation simultaneously, with F1 scores exceeding 87%. In the case of tracking a single case of misinformation, our framework reaches an F1 score of 83%. We collect and drive the algorithms with 15,952 misinformation-related tweets from the Boston Marathon bombing (2013), Manchester Arena bombing (2017), Hurricane Harvey (2017), Hurricane Irma (2017), and the Hawaii ballistic missile false alert (2018). This article provides novel insights on how to efficiently monitor misinformation that is spread during disasters.