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

Abeer AlDayel, Walid Magdy

Abstract: Highlights • We map out the current research terrain on stance detection and synthesize its relation to the existing theoretical orientations. • We provide a broader overview of stance detection methods, covering work that has been published in multiple research domains, including NLP, Computational Social Science,and Web science. • We survey the recent advances in stance detection methodologies and potential benchmarks by categorizing and summarizing the current methods. • We show the different applications of stance detection on social media and identify open key trends and challenges in the domain. 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, 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.