  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.
