论文标题
社交媒体上的立场检测：最新的和趋势的状态
Stance Detection on Social Media: State of the Art and Trends
论文作者
论文摘要
社交媒体上的立场检测是一种新兴的意见挖掘范式，用于各种社会和政治应用，其中情感分析可能是最佳的。在包括自然语言处理，网络科学和社交计算在内的多个社区中，开发有效的姿势检测方法的有效方法的研究兴趣越来越多。本文调查了这些社区内的立场检测的工作，并将其用法定位在社交媒体中当前的意见采矿技术中。它对社交媒体上的立场检测技术进行了详尽的审查，包括任务定义，立场检测中的不同类型的目标，所使用的功能以及应用的各种机器学习方法。调查报告在现有的基准数据集上有关立场检测的最先进的结果，并讨论了最有效的方法。此外，这项研究还探讨了在社交媒体上的立场检测的新兴趋势和不同的应用。该研究结束时讨论了当前现有研究的差距，并突出了社交媒体上立场检测的未来方向。
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.
