Quantifying Political Leaning from Tweets, Retweets, and Retweeters
Quantifying Political Leaning from Tweets, Retweets, and Retweeters
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DOI：
10.1109/tkde.2016.2553667
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发表时间：
2016-08-01
影响因子：
8.9
通讯作者：
Chiang, Mung
中科院分区：
文献类型：
--
作者：
Wong, Felix Ming Fai;Tan, Chee Wei;Chiang, Mung
公众、新闻媒体和政治行为者广泛使用在线社交网络（OSNs）来传播信息和交换意见，这为计算政治学的研究开辟了新的途径。本文研究了推特用户政治倾向的量化和推断问题。我们将政治学习推理作为一个凸优化问题，它包含两个想法：(a)用户在关于政治问题的推文和转发行为上是一致的，(b)相似的用户倾向于被相似的受众转发。然后，我们将我们的推理技术应用于2012年美国总统竞选期间七个月内收集的1.19亿条与选举相关的推文。在一组频繁转发的来源上，与手动创建标签相比，我们的技术达到了94%的准确率和高排名相关性。通过研究1000个频繁转发的信息源、232,000个转发这些信息源的普通用户的政治倾向，以及这些信息源使用的标签，我们的定量研究揭示了Twitter用户的政治人口统计特征，以及随着事件的发展，政治两极分化的时间动态。
The widespread use of online social networks (OSNs) to disseminate information and exchange opinions, by the general public, news media, and political actors alike, has enabled new avenues of research in computational political science. In this paper, we study the problem of quantifying and inferring the political leaning of Twitter users. We formulate political leaning inference as a convex optimization problem that incorporates two ideas: (a) users are consistent in their actions of tweeting and retweeting about political issues, and (b) similar users tend to be retweeted by similar audience. We then apply our inference technique to 119 million election-related tweets collected in seven months during the 2012 U.S. presidential election campaign. On a set of frequently retweeted sources, our technique achieves 94 percent accuracy and high rank correlation as compared with manually created labels. By studying the political leaning of 1,000 frequently retweeted sources, 232,000 ordinary users who retweeted them, and the hashtags used by these sources, our quantitative study sheds light on the political demographics of the Twitter population, and the temporal dynamics of political polarization as events unfold.