Stance detection on social media: State of the art and trends

Abeer ALDayel, Walid Magdy

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Stance detection on social media is an emerging opinion mining paradigm for various social and political applications in which sentiment analysis may be sub-optimal. There has been a growing research interest for developing effective methods for stance detection methods varying among multiple communities including natural language processing, web science, and social computing, where each modeled stance detection in different ways. In this paper, we survey the work on stance detection across those communities and present an exhaustive review of stance detection techniques on social media, including the task definition, different types of targets in stance detection, features set used, and various machine learning approaches applied. Our survey reports state-of-the-art results on the existing benchmark datasets on stance detection, and discusses the most effective approaches. In addition, we explore the emerging trends and different applications of stance detection on social media, including opinion mining and prediction and recently using it for fake news detection. The study concludes by discussing the gaps in the current existing research and highlights the possible future directions for stance detection on social media.
Original languageEnglish
Article number102597
Number of pages22
JournalInformation Processing and Management
Issue number4
Early online date13 Apr 2021
Publication statusPublished - 1 Jul 2021

Keywords / Materials (for Non-textual outputs)

  • Stance detection
  • Stance
  • Social media
  • Stance classification


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