论文标题
基于词汇的方法，用于量化社交媒体中的争议
Vocabulary-based Method for Quantifying Controversy in Social Media
论文作者
论文摘要
从社会的角度来看，识别有争议的主题不仅有趣，还可以应用方法来避免信息隔离，创建更好的讨论环境并在最佳情况下达成协议。在本文中，我们开发了一种系统的方法，主要基于社区中社区中使用的行话。我们的方法分配使用特定于领域的知识，是语言不可思议的，高效且易于应用的。我们在许多语言，区域和环境中进行了一系列实验，涉及有争议的和非争议的主题。我们发现，我们的基于词汇的测量的性能要比仅基于社区图结构的最新措施的状态更好。此外，我们表明可以通过文本分析检测极化。
Identifying controversial topics is not only interesting from a social point of view, it also enables the application of methods to avoid the information segregation, creating better discussion contexts and reaching agreements in the best cases. In this paper we develop a systematic method for controversy detection based primarily on the jargon used by the communities in social media. Our method dispenses with the use of domain-specific knowledge, is language-agnostic, efficient and easy to apply. We perform an extensive set of experiments across many languages, regions and contexts, taking controversial and non-controversial topics. We find that our vocabulary-based measure performs better than state of the art measures that are based only on the community graph structure. Moreover, we shows that it is possible to detect polarization through text analysis.
