Emotion Detection for Misinformation: A Review

TL;DR

This review paper discusses how emotion and sentiment analysis can be used to detect misinformation online, analyzing various methods, challenges, and future directions in emotion-based fake news detection.

Contribution

It provides a comprehensive overview of emotion-based misinformation detection methods, highlighting their strengths, weaknesses, and future research challenges.

Findings

Emotion analysis helps distinguish fake news from genuine news.

Emotion-based features improve misinformation detection accuracy.

Challenges include data collection, annotation, and interpretability.

Abstract

With the advent of social media, an increasing number of netizens are sharing and reading posts and news online. However, the huge volumes of misinformation (e.g., fake news and rumors) that flood the internet can adversely affect people's lives, and have resulted in the emergence of rumor and fake news detection as a hot research topic. The emotions and sentiments of netizens, as expressed in social media posts and news, constitute important factors that can help to distinguish fake news from genuine news and to understand the spread of rumors. This article comprehensively reviews emotion-based methods for misinformation detection. We begin by explaining the strong links between emotions and misinformation. We subsequently provide a detailed analysis of a range of misinformation detection methods that employ a variety of emotion, sentiment and stance-based features, and describe their strengths and weaknesses. Finally, we discuss a number of ongoing challenges in emotion-based misinformation detection based on large language models and suggest future research directions, including data collection (multi-platform, multilingual), annotation, benchmark, multimodality, and interpretability.