Emotion Detection for Misinformation: A Review
Zhiwei Liu, Tianlin Zhang, Kailai Yang, Paul Thompson, Zeping Yu, Sophia Ananiadou
TL;DR
Emotion Detection for Misinformation: A Review surveys how emotions and sentiment signals inform the detection of misinformation on social media. It inventorys datasets, conventional ML and deep learning methods, and advanced fusion techniques that combine emotion with content, temporal, propagation, and multimodal cues, including stance information. The review highlights dual-emotion mining, graph/temporal models, and multitask/multimodal frameworks, and discusses challenges such as data diversity, annotation quality, interpretability, and the potential of large language models. Overall, it argues that integrating affective signals with structural and multimodal context is essential for robust misinformation detection and outlines a roadmap for future research.
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.