# ExFake: Towards an Explainable Fake News Detection Based on Content and Social Context Information

arXiv: https://arxiv.org/abs/2311.10784 · HTML: https://mlsanity.com/paper/2311.10784/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 16 Nov 2023.

## Builds on (most cited first, 9 of 9)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media](https://mlsanity.com/paper/2004.11648/) (2020)
- [Beyond News Contents: The Role of Social Context for Fake News Detection](https://mlsanity.com/paper/1712.07709/) (2017)
- [The Role of User Profile for Fake News Detection](https://mlsanity.com/paper/1904.13355/) (2019)
- [Explainable Tsetlin Machine framework for fake news detection with credibility score assessment](https://mlsanity.com/paper/2105.09114/) (2021)
- [Linked Credibility Reviews for Explainable Misinformation Detection](https://mlsanity.com/paper/2008.12742/) (2020)

## Built on by (most cited first, 1 of 1)

- [Trust Oriented Explainable AI for Fake News Detection](https://mlsanity.com/paper/2603.11778/) (2026)

## Abstract (arXiv)

ExFake is an explainable fake news detection system based on content and context-level information. It is concerned with the veracity analysis of online posts based on their content, social context (i.e., online users' credibility and historical behaviour), and data coming from trusted entities such as fact-checking websites and named entities. Unlike state-of-the-art systems, an Explainable AI (XAI) assistant is also adopted to help online social networks (OSN) users develop good reflexes when faced with any doubted information that spreads on social networks. The trustworthiness of OSN users is also addressed by assigning a credibility score to OSN users, as OSN users are one of the main culprits for spreading fake news. Experimental analysis on a real-world dataset demonstrates that ExFake significantly outperforms other baseline methods for fake news detection.