DISCO: Comprehensive and Explainable Disinformation Detection

Dongqi Fu, Yikun Ban, Hanghang Tong, Ross MacIejewski, Jingrui He

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationCIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Number of pages5
ISBN (Electronic)9781450392365
StatePublished - Oct 17 2022
Event31st ACM International Conference on Information and Knowledge Management, CIKM 2022 - Atlanta, United States
Duration: Oct 17 2022Oct 21 2022

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings


Conference31st ACM International Conference on Information and Knowledge Management, CIKM 2022
Country/TerritoryUnited States


  • disinformation detection
  • explanation
  • graph augmentation

ASJC Scopus subject areas

  • General Business, Management and Accounting
  • General Decision Sciences


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