With the evolution of social media, cyberspace has become the default medium for social media users to communicate, especially during high-impact events such as pandemics, natural disasters, terrorist attacks, and periods of political unrest. However, during such events, misinformation can spread rapidly on social media, affecting decision-making and creating social unrest. Identifying and curtailing the spread of misinformation during high-impact events are significant data challenges given the scarcity and variety of the data, the speed by which misinformation can propagate, and the fairness aspects associated with this societal problem. Recent statistical machine learning advances have shown promise for misinformation detection; however, key limitations still make this a significant challenge. These limitations relate to using representative and bias-free multimodal data and to the explainability, fairness, and reliable performance of a system that detects misinformation. In this article, we critically discuss the current state-of-the-art approaches that attempt to respond to these complex requirements and present major unsolved issues; future research directions; and the synergies among statistics, data science, and other sciences for detecting misinformation.

中文翻译：

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统计在检测错误信息中的作用：最新技术、开放问题和未来研究方向回顾


随着社交媒体的发展，网络空间已成为社交媒体用户的默认交流媒介，尤其是在大流行病、自然灾害、恐怖袭击和政治动荡时期等高影响事件期间。然而，在此类事件期间，错误信息会在社交媒体上迅速传播，影响决策并造成社会动荡。鉴于数据的稀缺性和多样性、错误信息的传播速度以及与这一社会问题相关的公平性，识别和减少错误信息在高影响事件期间的传播是一项重大的数据挑战。最近的统计机器学习进展显示出错误信息检测的前景;但是，关键限制仍然使这成为一项重大挑战。这些限制与使用具有代表性和无偏见的多模式数据以及检测错误信息的系统的可解释性、公平性和可靠性能有关。在本文中，我们批判性地讨论了当前最先进的方法，这些方法试图响应这些复杂的需求并提出未解决的重大问题;未来的研究方向;以及统计学、数据科学和其他科学之间用于检测错误信息的协同作用。