# An Emotion-guided Approach to Domain Adaptive Fake News Detection using Adversarial Learning

arXiv: https://arxiv.org/abs/2211.17108 · HTML: https://mlsanity.com/paper/2211.17108/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 7 references that are in the map.
- First posted on arXiv 26 Nov 2022.

## Builds on (most cited first, 2 of 2)

- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [Mining Dual Emotion for Fake News Detection](https://mlsanity.com/paper/1903.01728/) (2019)

## Built on by (most cited first, 2 of 2)

- [Emotion Detection for Misinformation: A Review](https://mlsanity.com/paper/2311.00671/) (2023)
- [Rumor Detection by Multi-task Suffix Learning based on Time-series Dual Sentiments](https://mlsanity.com/paper/2502.14383/) (2025)

## Abstract (arXiv)

Recent works on fake news detection have shown the efficacy of using emotions as a feature for improved performance. However, the cross-domain impact of emotion-guided features for fake news detection still remains an open problem. In this work, we propose an emotion-guided, domain-adaptive, multi-task approach for cross-domain fake news detection, proving the efficacy of emotion-guided models in cross-domain settings for various datasets.