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, with a particular focus on advanced fusion methods. 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.

中文翻译：

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错误信息的情绪检测：综述

随着社交媒体的出现，越来越多的网民在网上分享和阅读帖子和新闻。然而，充斥互联网的大量错误信息（例如假新闻和谣言）会对人们的生活产生不利影响，并导致谣言和假新闻检测成为一个热门研究课题。社交媒体帖子和新闻中表达的网民情绪和情绪是有助于区分假新闻和真实新闻以及了解谣言传播的重要因素。本文全面回顾了基于情感的错误信息检测方法，特别关注先进的融合方法。我们首先解释情绪和错误信息之间的密切联系。随后，我们对一系列使用各种情感、情绪和基于立场的特征的错误信息检测方法进行了详细分析，并描述了它们的优点和缺点。最后，我们讨论了基于大语言模型的基于情感的错误信息检测中的一些持续挑战，并提出了未来的研究方向，包括数据收集（多平台、多语言）、注释、基准、多模态和可解释性。