Detection of Stance-Related Characteristics in Social Media Text
Detection of Stance-Related Characteristics in Social Media Text
复制标题
DOI：
10.1145/3200947.3201017
复制
发表时间：
2018-07
期刊：
影响因子：
--
通讯作者：
Vasiliki Simaki;Panagiotis Simakis;C. Paradis;A. Kerren
中科院分区：
文献类型：
--
作者：
Vasiliki Simaki;Panagiotis Simakis;C. Paradis;A. Kerren
在本文中，我们提出了一项识别社交媒体文本数据中与立场相关的特征的研究。根据我们之前关于立场的工作以及对立场模式的发现，我们在 Twitter 和 Facebook 的数据集中检测到了与立场相关的特征。我们提取了各种基于语料库、定量和计算的特征，这些特征被证明对六种立场类别（相反性、假设性、必然性、预测、知识来源和不确定性）具有重要意义，并在我们的数据集中测试了它们。介绍并讨论了初步聚类方法的结果，作为该领域未来贡献的起点。我们的实验结果表明不同特征和立场结构之间存在很强的相关性，这可以引导我们找到一种对这些数据进行自动立场注释的方法。
In this paper, we present a study for the identification of stance-related features in text data from social media. Based on our previous work on stance and our findings on stance patterns, we detected stance-related characteristics in a data set from Twitter and Facebook. We extracted various corpus-, quantitative- and computational-based features that proved to be significant for six stance categories (contrariety, hypotheticality, necessity, prediction, source of knowledge, and uncertainty), and we tested them in our data set. The results of a preliminary clustering method are presented and discussed as a starting point for future contributions in the field. The results of our experiments showed a strong correlation between different characteristics and stance constructions, which can lead us to a methodology for automatic stance annotation of these data.