# Mining Dual Emotion for Fake News Detection

## TL;DR

This paper introduces the concept of dual emotion, combining publisher and social emotions, as a novel feature for improving fake news detection, demonstrating its effectiveness across multiple datasets.

## Contribution

It proposes Dual Emotion Features that capture the relationship between publisher and social emotions, enhancing existing fake news detection methods.

## Key findings

- Dual emotion features outperform existing emotional features.
- The features improve detection accuracy when integrated into current models.
- Effective across datasets in different languages.

## Abstract

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.