DISCO: Comprehensive and Explainable Disinformation Detection
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Disinformation refers to false information deliberately spread to influence the general public, and the negative impact of disinformation on society can be observed for 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 prediction opaqueness. 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. We expect that our demo could pave the way for addressing the limitations of identification, comprehension, and explainability as a whole.
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