DISCO: Comprehensive and Explainable Disinformation Detection
- URL: http://arxiv.org/abs/2203.04928v1
- Date: Wed, 9 Mar 2022 18:17:25 GMT
- Title: DISCO: Comprehensive and Explainable Disinformation Detection
- Authors: Dongqi Fu, Yikun Ban, Hanghang Tong, Ross Maciejewski, Jingrui He
- Abstract summary: We propose a comprehensive and explainable disinformation detection framework called DISCO.
We demonstrate DISCO on a real-world fake news detection task with satisfactory detection accuracy and explanation.
We expect that our demo could pave the way for addressing the limitations of identification, comprehension, and explainability as a whole.
- Score: 71.5283511752544
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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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