Viewpoint Discovery and Understanding in Social Networks
Viewpoint Discovery and Understanding in Social Networks
复制标题
社交网络中的观点发现和理解
DOI：
10.1145/3201064.3201076
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
E. Herder
中科院分区：
文献类型：
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作者：
M. Quraishi;P. Fafalios;E. Herder
网络已经发展成为一个占主导地位的平台，每个人都有机会表达自己的意见，与其他用户互动，并就世界各地发生的新事件进行辩论。一方面，这使得对于通常有争议的话题（如英国脱欧）存在不同的观点和意见，但同时，它也导致了媒体偏见、回声室和过滤气泡等现象，即用户在同一话题上只能接触到一种观点。因此，需要能够检测和解释不同观点的方法。在本文中，我们提出了一种图划分方法，该方法利用社交互动来发现不同社区（代表不同观点）讨论 Twitter 等社交网络中的争议话题。为了解释发现的观点，我们描述了一种称为迭代排名差异（IRD）的方法，该方法允许检测表征不同观点的描述性术语，并了解特定术语与观点的关系（通过检测其他相关描述性术语）。实验评估结果表明，我们的方法在观点发现方面优于最先进的方法，而对三个不同争议主题所提出的 IRD 方法的定性分析表明，IRD 提供了不同观点的全面和深入的表示。
The Web has evolved to a dominant platform where everyone has the opportunity to express their opinions, to interact with other users, and to debate on emerging events happening around the world. On the one hand, this has enabled the presence of different viewpoints and opinions about a - usually controversial - topic (like Brexit), but at the same time, it has led to phenomena like media bias, echo chambers and filter bubbles, where users are exposed to only one point of view on the same topic. Therefore, there is the need for methods that are able to detect and explain the different viewpoints. In this paper, we propose a graph partitioning method that exploits social interactions to enable the discovery of different communities (representing different viewpoints) discussing about a controversial topic in a social network like Twitter. To explain the discovered viewpoints, we describe a method, called Iterative Rank Difference (IRD), which allows detecting descriptive terms that characterize the different viewpoints as well as understanding how a specific term is related to a viewpoint (by detecting other related descriptive terms). The results of an experimental evaluation showed that our approach outperforms state-of-the-art methods on viewpoint discovery, while a qualitative analysis of the proposed IRD method on three different controversial topics showed that IRD provides comprehensive and deep representations of the different viewpoints.