Comparing automated content analysis methods to distinguish issue communication by political parties on Twitter
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Praet, S., Van Aelst, P., Daelemans, W., Walgrave, S., Kreutz, T., Peeters, J., & Martens, D. (2021). Comparing automated content analysis methods to distinguish issue communication by political parties on Twitter. Computational Communication Research, 3(2), 195-219. Retrieved from https://computationalcommunication.org/ccr/article/view/45

Abstract

Party competition in Western Europe is increasingly focused on “issue competition”, which is the selective emphasis on issues by parties. The aim of this paper is to contribute methodologically to the increasing number of studies that deal with different aspects of parties’ issue competition and communication. We systematically compare the value and shortcomings of three exploratory text representation approaches to study the issue communication of parties on Twitter. More specifically, we analyze which issues separate the online communication of one party from that of the other parties and how consistent party communication is. Our analysis was performed on two years of Twitter data from six Belgian political parties, comprising of over 56,000 political tweets. The results indicate that our exploratory approach is useful to study how political parties profile themselves on Twitter and which strategies are at play. Second, our method allows to analyze communication of individual politicians which contributes to classical literature on party unity and party discipline. A comparison of our three methods shows a clear trade-off between interpretability and discriminative power, where a combination of all three simultaneously provides the best insights.

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