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首页> 外文会议>International Conference on Social, Cultural, and Behavioral Modeling Prediction and Behavior Representation in Modeling and Simulation >Ideology Detection for Twitter Users via Link Analysis
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Ideology Detection for Twitter Users via Link Analysis

机译：通过链接分析对推特用户的意识形态检测

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摘要

The problem of ideology detection is to study the latent (political) placement for people, which is traditionally studied on politicians according to their voting behaviors. Recently, more and more studies begin to address the ideology detection problem for ordinary users based on their online behaviors that can be captured by social media, e.g., Twitter. As far as we are concerned, the vast majority of the existing methods on ideology detection on social media have oversimplified the problem as a binary classification problem (i.e., liberal vs. conservative). Moreover, though social links can play a critical role in deciding one's ideology, most of the existing work ignores the heterogeneous types of links in social media. In this paper we propose to detect numerical ideology positions for Twitter users, according to their follow, mention, and retweet links to a selected set of politicians. A unified probabilistic model is proposed that can (1) integrate heterogeneous types of links together in determining people's ideology, and (2) automatically learn the quality of each type of links in deciding one's ideology. Experiments have demonstrated the advantages of our model in terms of both ranking and political leaning classification accuracy.
机译：思想探测的问题是研究人们的潜在（政治）安置，这些潜在人的潜在（政治）安排在传统上根据其投票行为研究了政治家。最近，越来越多的研究开始根据他们的在线行为来解决普通用户的意识形态检测问题，这些行为可以由社交媒体捕获，例如推特。据我们所知，绝大多数关于社交媒体的意识形态检测方法已经超越了作为二进制分类问题的问题（即，自由主义与保守派）。此外，虽然社交链接在决定一个人的意识形态方面发挥着关键作用，但大多数现有工作都忽略了社交媒体中的异构类型的联系。在本文中，我们建议根据他们的关注，提及和转发链接来检测Twitter用户的数字意识形态位置。提出了一个统一的概率模型，可以（1）将异构类型的链接集成在一起，在确定人的意识形态时，（2）在决定一个人的意识形态时自动学习每种类型的链路的质量。实验表明，在排名和政治倾向分类准确性方面表明了我们的模型的优势。

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