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Towards Smart Fake News Detection Through Explainable AI

arXiv 2207.11490 ↗First posted 23 Jul 2022cs.AI cs.IR cs.SIcited by 0

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Abstract (arXiv)

People now see social media sites as their sole source of information due to their popularity. The Majority of people get their news through social media. At the same time, fake news has grown exponentially on social media platforms in recent years. Several artificial intelligence-based solutions for detecting fake news have shown promising results. On the other hand, these detection systems lack explanation capabilities, i.e., the ability to explain why they made a prediction. This paper highlights the current state of the art in explainable fake news detection. We discuss the pitfalls in the current explainable AI-based fake news detection models and present our ongoing research on multi-modal explainable fake news detection model.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 19 references that are in the map. First posted on arXiv 23 Jul 2022.

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References · 5

  1. 2017"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detectioncited by 361
  2. 2020GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Mediacited by 108
  3. 2019Network-based Fake News Detection: A Pattern-driven Approachcited by 33
  4. 2019XFake: Explainable Fake News Detector with Visualizationscited by 32
  5. 2021Explainable Tsetlin Machine framework for fake news detection with credibility score assessmentcited by 23

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