Name
Abstract | ||
|---|---|---|
ABSTRACTStance detection (also known as stance classification, stance prediction, and stance analysis) is a problem related to social media analysis, natural language processing, and information retrieval, which aims to determine the position of a person from a piece of text they produce, towards a target (a concept, idea, event, etc.) either explicitly specified in the text, or implied only. Common stance classes include Favor, Against, and None. In this tutorial, we will define the core concepts and other related research problems, present historical and contemporary approaches to stance detection (including shared tasks and tools employed), provide pointers to related datasets, and cover open research directions and application areas of stance detection. As solutions to stance detection can contribute to diverse applications including trend analysis, opinion surveys, user reviews, personalization, and predictions for referendums and elections, it will continue to stand as an important research problem, mostly on textual content currently, and particularly on Web content including social media. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3488560.3501391 | WSDM |
Keywords | DocType | Citations |
Stance detection, Social media analysis, Twitter, Data streams | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |