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Stance Detection: A Survey

Authors:

Dilek Küçük,

Fazli CanAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 53, Issue 1

Article No.: 12, Pages 1 - 37

https://doi.org/10.1145/3369026

Published: 06 February 2020 Publication History

Abstract

Automatic elicitation of semantic information from natural language texts is an important research problem with many practical application areas. Especially after the recent proliferation of online content through channels such as social media sites, news portals, and forums; solutions to problems such as sentiment analysis, sarcasm/controversy/veracity/rumour/fake news detection, and argument mining gained increasing impact and significance, revealed with large volumes of related scientific publications. In this article, we tackle an important problem from the same family and present a survey of stance detection in social media posts and (online) regular texts. Although stance detection is defined in different ways in different application settings, the most common definition is “automatic classification of the stance of the producer of a piece of text, towards a target, into one of these three classes: {Favor, Against, Neither}.” Our survey includes definitions of related problems and concepts, classifications of the proposed approaches so far, descriptions of the relevant datasets and tools, and related outstanding issues. Stance detection is a recent natural language processing topic with diverse application areas, and our survey article on this newly emerging topic will act as a significant resource for interested researchers and practitioners.

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Index Terms

Stance Detection: A Survey
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
  2. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Sentiment analysis
    2. Specialized information retrieval
      1. Environment-specific retrieval
        Web and social media search

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 53, Issue 1

January 2021

781 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3382040

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 06 February 2020

Accepted: 01 October 2019

Revised: 01 July 2019

Received: 01 January 2019

Published in CSUR Volume 53, Issue 1

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