A Survey of Stance Detection on Social Media: New Directions and Perspectives

TL;DR

This survey reviews current stance detection techniques on social media, emphasizing recent advances with large language models, and discusses future challenges like multi-modal and low-resource language detection.

Contribution

It provides a comprehensive overview of stance detection methods, datasets, and future research directions, highlighting gaps and opportunities in the field.

Findings

Traditional models have limitations in generalizability.

Large language models improve detection accuracy.

Emerging challenges include multi-modal and low-resource language detection.

Abstract

In modern digital environments, users frequently express opinions on contentious topics, providing a wealth of information on prevailing attitudes. The systematic analysis of these opinions offers valuable insights for decision-making in various sectors, including marketing and politics. As a result, stance detection has emerged as a crucial subfield within affective computing, enabling the automatic detection of user stances in social media conversations and providing a nuanced understanding of public sentiment on complex issues. Recent years have seen a surge of research interest in developing effective stance detection methods, with contributions from multiple communities, including natural language processing, web science, and social computing. This paper provides a comprehensive survey of stance detection techniques on social media, covering task definitions, datasets, approaches, and future works. We review traditional stance detection models, as well as state-of-the-art methods based on large language models, and discuss their strengths and limitations. Our survey highlights the importance of stance detection in understanding public opinion and sentiment, and identifies gaps in current research. We conclude by outlining potential future directions for stance detection on social media, including the need for more robust and generalizable models, and the importance of addressing emerging challenges such as multi-modal stance detection and stance detection in low-resource languages.