Two Computational Models for Analyzing Political Attention in Social Media
Two Computational Models for Analyzing Political Attention in Social Media
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DOI：
10.1609/icwsm.v14i1.7297
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Libby Hemphill;Angela M. Schöpke-Gonzalez
中科院分区：
文献类型：
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作者：
Libby Hemphill;Angela M. Schöpke-Gonzalez
了解政治注意力是如何划分的，以及关于哪些主题的研究对于议程设置、框架和政治修辞等领域的研究至关重要。现有的测量注意力的方法，如根据已建立的码本进行手动标记，成本很高，而且可能会受到限制。我们描述了两个自动区分政客社交媒体内容中的主题的计算模型。我们的模型-一个监督分类器和一个非监督主题模型-提供了不同的好处。监督分类器减少了根据预定主题列表对内容进行分类所需的工作量。然而，推文不仅仅是传达政策立场。我们的无监督模型既揭示了政治话题，也揭示了Twitter的其他用途(例如，选民服务)。这些模型是政治沟通和社交媒体研究的有效、廉价的计算工具。我们通过将这两个模型应用于第115届美国国会议员发布的推文，展示了它们的实用性，并讨论了它们提供的不同分析。
Understanding how political attention is divided and over what subjects is crucial for research on areas such as agenda setting, framing, and political rhetoric. Existing methods for measuring attention, such as manual labeling according to established codebooks, are expensive and can be restrictive. We describe two computational models that automatically distinguish topics in politicians' social media content. Our models—one supervised classifier and one unsupervised topic model—provide different benefits. The supervised classifier reduces the labor required to classify content according to pre-determined topic list. However, tweets do more than communicate policy positions. Our unsupervised model uncovers both political topics and other Twitter uses (e.g., constituent service). These models are effective, inexpensive computational tools for political communication and social media research. We demonstrate their utility and discuss the different analyses they afford by applying both models to the tweets posted by members of the 115th U.S. Congress.