# Towards Smart Fake News Detection Through Explainable AI

arXiv: https://arxiv.org/abs/2207.11490 · HTML: https://mlsanity.com/paper/2207.11490/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 23 Jul 2022.

## Builds on (most cited first, 5 of 5)

- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media](https://mlsanity.com/paper/2004.11648/) (2020)
- [Network-based Fake News Detection: A Pattern-driven Approach](https://mlsanity.com/paper/1906.04210/) (2019)
- [XFake: Explainable Fake News Detector with Visualizations](https://mlsanity.com/paper/1907.07757/) (2019)
- [Explainable Tsetlin Machine framework for fake news detection with credibility score assessment](https://mlsanity.com/paper/2105.09114/) (2021)

## 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.