捍卫民主：使用深度学习识别和防止错误信息
Defending Democracy: Using Deep Learning to Identify and Prevent Misinformation
摘要：
近年来，网上错误信息的增加通过扭曲真实的公共话语，造成混乱、恐惧，甚至在极端情况下造成暴力，威胁着民主国家。有必要了解虚假内容通过在线网络的传播，以便制定干预措施，在错误信息达到病毒式传播之前将其破坏。使用深度双向Transformer语言理解（BERT）和传播图，这项研究分类和可视化的传播错误信息的社交媒体网络使用公开的Twitter数据。结果证实了之前关于用户集群和虚假内容病毒式传播的研究，同时提高了深度学习模型用于错误信息检测的精度。该研究进一步证明了BERT为虚假信息检测提供可扩展模型的适用性，这有助于开发更及时，更准确的干预措施，以减缓在线环境中错误信息的传播。
英文摘要：
The rise in online misinformation in recent years threatens democracies by distorting authentic public discourse and causing confusion, fear, and even, in extreme cases, violence. There is a need to understand the spread of false content through online networks for developing interventions that disrupt misinformation before it achieves virality. Using a Deep Bidirectional Transformer for Language Understanding (BERT) and propagation graphs, this study classifies and visualizes the spread of misinformation on a social media network using publicly available Twitter data. The results confirm prior research around user clusters and the virality of false content while improving the precision of deep learning models for misinformation detection. The study further demonstrates the suitability of BERT for providing a scalable model for false information detection, which can contribute to the development of more timely and accurate interventions to slow the spread of misinformation in online environments.
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。
