论文标题
迪斯科：全面且可解释的虚假信息检测
DISCO: Comprehensive and Explainable Disinformation Detection
论文作者
论文摘要
虚假信息是指故意传播的虚假信息以影响公众，而虚假信息对社会的负面影响可以在许多问题（例如政治议程和操纵金融市场）中观察到。在本文中，我们确定了从多个方面的自动虚假信息检测而引起的普遍挑战和进步，并提出了一个称为迪斯科的全面且可解释的虚假发现检测框架。它利用了虚假信息的异质性，并解决了预测的不透明性。然后，我们以令人满意的检测准确性和解释为现实世界中的假新闻检测任务提供了迪斯科舞厅的演示。迪斯科的演示视频和源代码现在已公开可用https://github.com/dongqifu/disco。我们预计我们的演示可以为解决整体识别，理解和解释性的局限性铺平道路。
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.
