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.

社交媒体上的姿态检测是一种针对各种社会和政治应用的新兴观点挖掘范例，其中情感分析可能不是最佳的。对于开发有效的姿态检测方法的方法，研究兴趣日益增长，该方法在多个社区之间变化，包括自然语言处理，网络科学和社交计算，其中每种建模方式均以不同的方式进行。在本文中，我们调查了这些社区中的姿态检测工作，并详尽地回顾了社交媒体上的姿态检测技术，包括任务定义，姿态检测中的不同类型目标，使用的功能集以及所应用的各种机器学习方法。我们的调查报告了有关姿态检测的现有基准数据集的最新结果，并讨论最有效的方法。此外，我们探索了社交媒体上姿势检测的新兴趋势和不同应用，包括观点挖掘和预测，最近将其用于伪造新闻检测。该研究通过讨论当前现有研究中的差距得出结论，并强调了社交媒体上姿态检测的未来可能方向。