# Emotion-guided Cross-domain Fake News Detection using Adversarial Domain Adaptation

arXiv: https://arxiv.org/abs/2211.13718 · HTML: https://mlsanity.com/paper/2211.13718/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 24 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [Zoom Out and Observe: News Environment Perception for Fake News Detection](https://mlsanity.com/paper/2203.10885/) (2022)
- [Emotion Cognizance Improves Health Fake News Identification](https://mlsanity.com/paper/1906.10365/) (2019)
- [Mining Dual Emotion for Fake News Detection](https://mlsanity.com/paper/1903.01728/) (2019)
- [A Deep Learning Approach for Automatic Detection of Fake News](https://mlsanity.com/paper/2005.04938/) (2020)

## Built on by (most cited first, 4 of 4)

- [Emotion Detection for Misinformation: A Review](https://mlsanity.com/paper/2311.00671/) (2023)
- [Prompt-Induced Linguistic Fingerprints for LLM-Generated Fake News Detection](https://mlsanity.com/paper/2508.12632/) (2025)
- [Dual-Teacher De-biasing Distillation Framework for Multi-domain Fake News Detection](https://mlsanity.com/paper/2312.01006/) (2023)
- [Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval-Augmented Generation](https://mlsanity.com/paper/2410.03461/) (2024)

## Abstract (arXiv)

Recent works on fake news detection have shown the efficacy of using emotions as a feature or emotions-based features for improved performance. However, the impact of these emotion-guided features for fake news detection in cross-domain settings, where we face the problem of domain shift, is still largely unexplored. In this work, we evaluate the impact of emotion-guided features for cross-domain fake news detection, and further propose an emotion-guided, domain-adaptive approach using adversarial learning. We prove the efficacy of emotion-guided models in cross-domain settings for various combinations of source and target datasets from FakeNewsAMT, Celeb, Politifact and Gossipcop datasets.