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Welcome to the Party: Modeling the Topic Evolution of Political Parties in Microblogs Using Dynamic Topic Models

Welcome to the Party: Modeling the Topic Evolution of Political Parties in Microblogs Using Dynamic Topic Models

Kai Heinrich
Copyright: © 2015 |Pages: 20
ISBN13: 9781466666399|ISBN10: 1466666390|EISBN13: 9781466666405
DOI: 10.4018/978-1-4666-6639-9.ch004
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MLA

Heinrich, Kai. "Welcome to the Party: Modeling the Topic Evolution of Political Parties in Microblogs Using Dynamic Topic Models." Recent Advances in Intelligent Technologies and Information Systems, edited by Vijayan Sugumaran, IGI Global, 2015, pp. 63-82. https://doi.org/10.4018/978-1-4666-6639-9.ch004

APA

Heinrich, K. (2015). Welcome to the Party: Modeling the Topic Evolution of Political Parties in Microblogs Using Dynamic Topic Models. In V. Sugumaran (Ed.), Recent Advances in Intelligent Technologies and Information Systems (pp. 63-82). IGI Global. https://doi.org/10.4018/978-1-4666-6639-9.ch004

Chicago

Heinrich, Kai. "Welcome to the Party: Modeling the Topic Evolution of Political Parties in Microblogs Using Dynamic Topic Models." In Recent Advances in Intelligent Technologies and Information Systems, edited by Vijayan Sugumaran, 63-82. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-6639-9.ch004

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Abstract

Modeling topic distributions over documents has become a recent method for coping with the problematic of huge amounts of unstructured data. Especially in the context of Web communities, topic models can capture the zeitgeist as a snapshot of people's communication. However, the problem that arises from that static snapshot is that it fails to capture the dynamics of a community. To cope with this problem, dynamic topic models were introduced. This chapter makes use of those topic models in order to capture dynamics in user behavior within microblog communities such as Twitter. However, only applying topic models yields no interpretable results, so a method is proposed that compares different political parties over time using regression models based on DTM output. For evaluation purposes, a Twitter data set divided into different political communities is analyzed and results and findings are presented.

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