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

TL;DR

This paper provides a comprehensive survey of stance detection techniques on social media, analyzing current methods, results, and emerging trends to guide future research in opinion mining.

Contribution

It offers an exhaustive review of stance detection approaches, benchmarks, and trends, highlighting gaps and future directions in social media opinion mining.

Findings

State-of-the-art results on benchmark datasets

Effective machine learning approaches identified

Emerging trends and applications discussed

Abstract

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.