DISCO: Comprehensive and Explainable Disinformation Detection
DISCO: Comprehensive and Explainable Disinformation Detection
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DOI：
10.1145/3511808.3557202
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发表时间：
2022-03
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虚假信息是指故意传播的虚假信息，旨在影响公众，其对社会的负面影响在诸多问题中都可见一斑，比如政治议程和操纵金融市场等方面。在本文中，我们从多个方面确定了与自动化虚假信息检测相关的普遍挑战和进展，并提出了一个名为DISCO的全面且可解释的虚假信息检测框架。它利用了虚假信息的异质性，并解决了预测的不透明性问题。然后，我们在一个真实世界的假新闻检测任务中对DISCO进行了演示，取得了令人满意的检测准确率和解释性。DISCO的演示视频和源代码现已公开，网址为https://github.com/DongqiFu/DISCO。我们期望我们的演示能够为解决整体上在识别、理解和可解释性方面的局限性铺平道路。
Disinformation refers to false information deliberately spread to influence the general public, and the negative impact of disinformation on society can be observed in numerous issues, such as political agendas and manipulating financial markets. In this paper, we identify prevalent challenges and advances related to automated disinformation detection from multiple aspects and propose a comprehensive and explainable disinformation detection framework called DISCO. It leverages the heterogeneity of disinformation and addresses the opaqueness of prediction. Then we provide a demonstration of DISCO on a real-world fake news detection task with satisfactory detection accuracy and explanation. The demo video and source code of DISCO is now publicly available https://github.com/DongqiFu/DISCO. We expect that our demo could pave the way for addressing the limitations of identification, comprehension, and explainability as a whole.