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I like, therefore I am. Predictive modeling to gain insights in political preference in a multi-party system

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  • PRAET, Stiene
  • VAN AELST, Peter
  • MARTENS, David

Abstract

In political sciences there is a long tradition of trying to understand party preferences and voting behavior to explain political decisions. Traditionally, scholars relied on voting histories, religious affiliation, and socio-economic status to understand people’s vote. Today, thanks to the Internet and social media, an unseen amount and granularity of data is available. In this paper we show how political insights can be gained from high-dimensional and sparse Facebook data, by building and interpreting predictive models based on Facebook ‘like’ and survey data of more than 6.500 Flemish participants. First, we built several logistic regression models to show that it is possible to predict political leaning and party preference based on Facebook likes in a multi-party system, even when excluding the political Facebook likes. Secondly, by introducing several metrics that measure the association between Facebook likes and a certain political affiliation, we can describe voter profiles in terms of common interests. For example, left voters often like environmental organizations and alternative rock music, whereas right voters like Flemish nationalistic content and techno music. Lastly, we develop a method to measure ideological homogeneity, or to what extent do people that like the same products, movies, books, etc. have a similar political ideology. In the Flemish setting, the categories ‘politics’ and ‘civil society’ are most ideologically homogeneous whereas ‘TV shows’ and ‘sports’ are the most heterogeneous. The results show that our approach has the potential to help political scientists to gain insights into voter profiles and ideological homogeneity using Facebook likes.

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  • PRAET, Stiene & VAN AELST, Peter & MARTENS, David, 2018. "I like, therefore I am. Predictive modeling to gain insights in political preference in a multi-party system," Working Papers 2018014, University of Antwerp, Faculty of Business and Economics.
  • Handle: RePEc:ant:wpaper:2018014
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    1. Foster Provost & David Martens & Alan Murray, 2015. "Finding Similar Mobile Consumers with a Privacy-Friendly Geosocial Design," Information Systems Research, INFORMS, vol. 26(2), pages 243-265, June.
    2. McNicholas, P.D. & Murphy, T.B. & O'Regan, M., 2008. "Standardising the lift of an association rule," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4712-4721, June.
    3. Jakob Bæk Kristensen & Thomas Albrechtsen & Emil Dahl-Nielsen & Michael Jensen & Magnus Skovrind & Tobias Bornakke, 2017. "Parsimonious data: How a single Facebook like predicts voting behavior in multiparty systems," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-12, September.
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