  Detecting attitudes expressed in texts, also known as stance detection, has
become an important task for the detection of false information online, be it
misinformation (unintentionally false) or disinformation (intentionally false,
spread deliberately with malicious intent). Stance detection has been framed in
different ways, including: (a) as a component of fact-checking, rumour
detection, and detecting previously fact-checked claims; or (b) as a task in
its own right. While there have been prior efforts to contrast stance detection
with other related social media tasks such as argumentation mining and
sentiment analysis, there is no survey examining the relationship between
stance detection detection and mis- and disinformation detection from a
holistic viewpoint, which is the focus of this survey. We review and analyse
existing work in this area, before discussing lessons learnt and future
challenges.
