论文标题
网络和社交媒体中的立场检测：比较研究
Stance Detection in Web and Social Media: A Comparative Study
论文作者
论文摘要
在线论坛和社交媒体平台越来越多地被用来讨论不同人采取不同立场的不同极性的主题。文献中已经提出了几种从文本中检测自动立场检测的方法。据我们所知，对其可重复性及其比较性能没有任何系统的调查。在这项工作中，我们探讨了几种现有的立场检测模型的可重复性，包括神经模型和基于经典分类器的模型。通过在两个数据集上进行实验 - （i）〜流行的半eval微博数据集，以及（ii）〜一组与健康相关的在线新闻文章 - 我们还对各种方法进行了详细的比较分析，并探索了它们的缺点。本文讨论的所有算法的实现可在https://github.com/prajwal1210/stance-detection-in-web-and-social-media上找到。
Online forums and social media platforms are increasingly being used to discuss topics of varying polarities where different people take different stances. Several methodologies for automatic stance detection from text have been proposed in literature. To our knowledge, there has not been any systematic investigation towards their reproducibility, and their comparative performances. In this work, we explore the reproducibility of several existing stance detection models, including both neural models and classical classifier-based models. Through experiments on two datasets -- (i)~the popular SemEval microblog dataset, and (ii)~a set of health-related online news articles -- we also perform a detailed comparative analysis of various methods and explore their shortcomings. Implementations of all algorithms discussed in this paper are available at https://github.com/prajwal1210/Stance-Detection-in-Web-and-Social-Media.
