用可解释AI同时检测假新闻和曝光信息，提升网络安全
FNDEX: Fake News and Doxxing Detection with Explainable AI
- 融合三个Transformer模型实现双类检测
- 在真实数据集上性能显著超越现有基线
- 生成可读解释，适合安全监管与平台审核
当前在线媒体与通信技术的广泛发展带来了言论自由边界界定的挑战，使互联网可能成为网络武器。在此背景下，假新闻和曝光（doxxing）两种危害性现象日益突出。尽管两者已有大量研究，但其交叉领域仍属空白。本文提出一种新型系统——可解释人工智能驱动的假新闻与曝光检测系统（FNDEX），利用三种不同Transformer模型实现对两类威胁的高性能检测。为保障数据安全，采用基于模式的三步匿名化流程，有效去除个人身份信息。同时强调检测结果的可解释性，生成清晰可读的决策依据。在真实数据集上的实验表明，该系统显著优于现有基准方法。
原文摘要 · Abstract (English)
The widespread and diverse online media platforms and other internet-driven communication technologies have presented significant challenges in defining the boundaries of freedom of expression. Consequently, the internet has been transformed into a potential cyber weapon. Within this evolving landscape, two particularly hazardous phenomena have emerged: fake news and doxxing. Although these threats have been subjects of extensive scholarly analysis, the crossroads where they intersect remain unexplored. This research addresses this convergence by introducing a novel system. The Fake News and Doxxing Detection with Explainable Artificial Intelligence (FNDEX) system leverages the capabilities of three distinct transformer models to achieve high-performance detection for both fake news and doxxing. To enhance data security, a rigorous three-step anonymization process is employed, rooted in a pattern-based approach for anonymizing personally identifiable information. Finally, this research emphasizes the importance of generating coherent explanations for the outcomes produced by both detection models. Our experiments on realistic datasets demonstrate that our system significantly outperforms the existing baselines
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。