Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data
Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data
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DOI：
10.1093/pan/mpu011
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发表时间：
2015-12-01
影响因子：
5.4
通讯作者：
Barbera, Pablo
中科院分区：
文献类型：
--
作者：
Barbera, Pablo
政治家和公民越来越多地在Twitter等社交媒体上进行政治对话。在这篇文章中，我表明，他们所嵌入的社交网络的结构可以成为他们的意识形态立场的信息来源。假设社交网络是同性恋，我开发了一个贝叶斯空间跟随模型，认为意识形态作为一个潜在的变量，其价值可以推断，通过检查每个用户的政治演员如下。这种方法使我们能够在任何时间点和许多政体中估计比任何现有替代方案更多的行为者的意识形态。我应用这种方法来估计美国和五个欧洲国家的精英和大众公共Twitter用户的大样本的理想点。立法者和政党的估计立场复制了传统的意识形态衡量标准。该方法还能够成功地对公开表明其政治偏好的个人以及与其政党注册记录相匹配的用户样本进行分类。为了说明这些估计的潜在贡献，我研究了2012年美国总统大选期间的在线行为在多大程度上是沿着意识形态路线聚集的。
Politicians and citizens increasingly engage in political conversations on social media outlets such as Twitter. In this article, I show that the structure of the social networks in which they are embedded can be a source of information about their ideological positions. Under the assumption that social networks are homophilic, I develop a Bayesian Spatial Following model that considers ideology as a latent variable, whose value can be inferred by examining which politics actors each user is following. This method allows us to estimate ideology for more actors than any existing alternative, at any point in time and across many polities. I apply this method to estimate ideal points for a large sample of both elite and mass public Twitter users in the United States and five European countries. The estimated positions of legislators and political parties replicate conventional measures of ideology. The method is also able to successfully classify individuals who state their political preferences publicly and a sample of users matched with their party registration records. To illustrate the potential contribution of these estimates, I examine the extent to which online behavior during the 2012 US presidential election campaign is clustered along ideological lines.