Quantifying Content Polarization on Twitter
Quantifying Content Polarization on Twitter
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DOI：
10.1109/cic.2017.00047
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发表时间：
2017-10
期刊：
影响因子：
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通讯作者：
Muhe Yang;Xidao Wen;Y. Lin;Lingjia Deng
中科院分区：
文献类型：
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作者：
Muhe Yang;Xidao Wen;Y. Lin;Lingjia Deng
Facebook 和 Twitter 等社交媒体已成为主要战场，向具有不同兴趣和意识形态的人们传播日益两极分化的内容。这项工作从独特的“内容”角度审视了 2016 年美国总统大选期间内容两极分化的程度。我们提出了一种新方法，通过利用词嵌入表示和聚类指标来量化内容语义的极化。然后，我们提出一个评估框架，以使用立场分类任务验证所提出的定量测量。根据结果，我们进一步探讨了选举期间内容两极分化的程度，以及它如何随着时间、地理位置和不同类型用户的变化而变化。这项工作有助于理解基于用户生成内容的在线“回声室”现象。
Social media like Facebook and Twitter have become major battlegrounds, with increasingly polarized content disseminated to people having different interests and ideologies. This work examines the extent of content polarization during the 2016 U.S. presidential election, from a unique, "content" perspective. We propose a new approach to quantify the polarization of content semantics by leveraging the word embedding representation and clustering metrics. We then propose an evaluation framework to verify the proposed quantitative measurement using a stance classification task. Based on the results, we further explore the extent of content polarization during the election period and how it changed across time, geography, and different types of users. This work contributes to understanding the online "echo chamber" phenomenon based on user-generated content.