A Survey on Explainable Fake News Detection

Ken Mishima, Hayato Yamana

Research output: Contribution to journalReview articlepeer-review

5 Citations (Scopus)


The increasing amount of fake news is a growing problem that will progressively worsen in our interconnected world. Machine learning, particularly deep learning, is being used to detect misinformation; however, the models employed are essentially black boxes, and thus are uninterpretable. This paper presents an overview of explainable fake news detection models. Specifically, we first review the existing models, datasets, evaluation techniques, and visualization processes. Subsequently, possible improvements in this field are identified and discussed.

Original languageEnglish
Pages (from-to)1249-1257
Number of pages9
JournalIEICE Transactions on Information and Systems
Issue number7
Publication statusPublished - 2022


  • attention mechanisms
  • explainability
  • fake news detection
  • misinformation detection

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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