# A Deep Learning Approach for Automatic Detection of Fake News

arXiv: https://arxiv.org/abs/2005.04938 · HTML: https://mlsanity.com/paper/2005.04938/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2020.

## Built on by (most cited first, 7 of 7)

- [Weakly Supervised Veracity Classification with LLM-Predicted Credibility Signals](https://mlsanity.com/paper/2309.07601/) (2023)
- [RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning Based on Emotional Information](https://mlsanity.com/paper/2406.11093/) (2024)
- [Fake News Detection System using XLNet model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Task](https://mlsanity.com/paper/2101.11425/) (2021)
- [Emotion-guided Cross-domain Fake News Detection using Adversarial Domain Adaptation](https://mlsanity.com/paper/2211.13718/) (2022)
- [HKD4VLM: A Progressive Hybrid Knowledge Distillation Framework for Robust Multimodal Hallucination and Factuality Detection in VLMs](https://mlsanity.com/paper/2506.13038/) (2025)
- [A Commonsense-Infused Language-Agnostic Learning Framework for Enhancing Prediction of Political Polarity in Multilingual News Headlines](https://mlsanity.com/paper/2212.00298/) (2022)
- [An Emotion-Aware Multi-Task Approach to Fake News and Rumour Detection using Transfer Learning](https://mlsanity.com/paper/2211.12374/) (2022)

## Abstract (arXiv)

Fake news detection is a very prominent and essential task in the field of journalism. This challenging problem is seen so far in the field of politics, but it could be even more challenging when it is to be determined in the multi-domain platform. In this paper, we propose two effective models based on deep learning for solving fake news detection problem in online news contents of multiple domains. We evaluate our techniques on the two recently released datasets, namely FakeNews AMT and Celebrity for fake news detection. The proposed systems yield encouraging performance, outperforming the current handcrafted feature engineering based state-of-the-art system with a significant margin of 3.08% and 9.3% by the two models, respectively. In order to exploit the datasets, available for the related tasks, we perform cross-domain analysis (i.e. model trained on FakeNews AMT and tested on Celebrity and vice versa) to explore the applicability of our systems across the domains.