2021/02/27 by Momchil Hardalov, Hardalov, Momchil, Arnav Arora +5 · 7 citations
Computer Science · Social Sciences · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Misinformation and Its Impacts #Social and Information Networks (cs.SI) #Spam and Phishing Detection #Topic Modeling #cs.CL #cs.SI
paper · pdf · doi:10.48550/arxiv.2103.00242
Accepted to NAACL-HLT 2022 (Findings)
openalex publication_date 2021/02/27 · arxiv created 2022/05/08 · arxiv updated 2022/05/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Understanding attitudes expressed in texts, also known as stance detection, plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentionally false information). 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 tasks such as argumentation mining and sentiment analysis, there is no existing survey on examining the relationship between stance detection and mis- and disinformation detection. Here, we aim to bridge this gap by reviewing and analysing existing work in this area, with mis- and disinformation in focus, and discussing lessons learnt and future challenges.