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.

翻译：随着社交媒体的兴起，越来越多的网民在线分享和阅读帖文与新闻。然而，大量虚假信息（例如假新闻和谣言）充斥互联网，对人们的生活造成负面影响，使得谣言与假新闻检测成为热门研究课题。网民在社交媒体帖文及新闻中表达的情绪与情感，是辨别真假新闻、理解谣言传播的重要依据。本文全面综述了基于情感信息的虚假信息检测方法。我们首先阐释了情感与虚假信息之间的紧密关联，随后详细分析了利用多种情感、情绪及立场特征构建的虚假信息检测方法，并阐述了其优缺点。最后，我们讨论了基于大语言模型的情感虚假信息检测领域当前面临的若干挑战，并提出了未来研究方向，包括数据收集（多平台、多语言）、标注、基准测试、多模态及可解释性。