# Mining Dual Emotion for Fake News Detection

arXiv: https://arxiv.org/abs/1903.01728 · HTML: https://mlsanity.com/paper/1903.01728/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2019.

## Built on by (most cited first, 8 of 8)

- [Emotion Detection for Misinformation: A Review](https://mlsanity.com/paper/2311.00671/) (2023)
- [Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference](https://mlsanity.com/paper/2410.07673/) (2024)
- [How does Truth Evolve into Fake News? An Empirical Study of Fake News Evolution](https://mlsanity.com/paper/2103.05944/) (2021)
- [Emotion-guided Cross-domain Fake News Detection using Adversarial Domain Adaptation](https://mlsanity.com/paper/2211.13718/) (2022)
- [An Emotion-guided Approach to Domain Adaptive Fake News Detection using Adversarial Learning](https://mlsanity.com/paper/2211.17108/) (2022)
- ["Beware of deception": Detecting Half-Truth and Debunking it through Controlled Claim Editing](https://mlsanity.com/paper/2308.07973/) (2023)
- [UrduFake@FIRE2020: Shared Track on Fake News Identification in Urdu](https://mlsanity.com/paper/2207.12406/) (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)

Emotion plays an important role in detecting fake news online. When leveraging emotional signals, the existing methods focus on exploiting the emotions of news contents that conveyed by the publishers (i.e., publisher emotion). However, fake news often evokes high-arousal or activating emotions of people, so the emotions of news comments aroused in the crowd (i.e., social emotion) should not be ignored. Furthermore, it remains to be explored whether there exists a relationship between publisher emotion and social emotion (i.e., dual emotion), and how the dual emotion appears in fake news. In this paper, we verify that dual emotion is distinctive between fake and real news and propose Dual Emotion Features to represent dual emotion and the relationship between them for fake news detection. Further, we exhibit that our proposed features can be easily plugged into existing fake news detectors as an enhancement. Extensive experiments on three real-world datasets (one in English and the others in Chinese) show that our proposed feature set: 1) outperforms the state-of-the-art task-related emotional features; 2) can be well compatible with existing fake news detectors and effectively improve the performance of detecting fake news.