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.
翻译:虚假信息指故意传播虚假信息以影响公众,虚假信息对社会的负面影响在政治议程和操纵金融市场等诸多问题上可见,本文指出与从多个方面自动发现虚假信息有关的普遍挑战和进展,提出一个名为DISCO的全面和可解释的虚假信息检测框架,利用虚假信息的多样性,解决预测的不透明性。然后,我们展示DISCO关于真实世界假新闻检测任务的演示,并给出令人满意的检测准确性和解释。DISCO的演示视频和源代码现已公开提供,https://github.com/DongqiFu/DISCO。我们期望我们的演示能够为解决识别、理解和解释的整体局限性铺平道路。