Stance Detection on Social Media: State of the Art and Trends

By Abeer Aldayel, Walid Magdy. Information Processing & Management, 2020

340 citations recorded by the source corpus.

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. This paper surveys the work on stance detection within those communities and situates its usage within current opinion mining techniques in social media. It presents 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. The survey reports state-of-the-art results on the existing benchmark datasets on stance detection, and discusses the most effective approaches. In addition, this study explores the emerging trends and different applications of stance detection on social media. The study concludes by discussing the gaps in the current existing research and highlights the possible future directions for stance detection on social media.

Subfields: Artificial Intelligence, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/6c125ddf8fdf6e26a74ee77359d947c9449a259a

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In Artificial Intelligence

  1. Alexandre Matton
  2. Valentina Semenova
  3. Renaud Lambiotte
  4. Liwen Jing
  5. Rachel Han
  6. Daijun Ding
  7. John Pougué-Biyong
  8. Doyne Farmer

In Sociology and Political Science

  1. Renaud Lambiotte

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