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Parsimonious data: How a single Facebook like predicts voting behavior in multiparty systems

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  • Jakob Bæk Kristensen
  • Thomas Albrechtsen
  • Emil Dahl-Nielsen
  • Michael Jensen
  • Magnus Skovrind
  • Tobias Bornakke

Abstract

This study shows how liking politicians’ public Facebook posts can be used as an accurate measure for predicting present-day voter intention in a multiparty system. We highlight that a few, but selective digital traces produce prediction accuracies that are on par or even greater than most current approaches based upon bigger and broader datasets. Combining the online and offline, we connect a subsample of surveyed respondents to their public Facebook activity and apply machine learning classifiers to explore the link between their political liking behaviour and actual voting intention. Through this work, we show that even a single selective Facebook like can reveal as much about political voter intention as hundreds of heterogeneous likes. Further, by including the entire political like history of the respondents, our model reaches prediction accuracies above previous multiparty studies (60–70%).The main contribution of this paper is to show how public like-activity on Facebook allows political profiling of individual users in a multiparty system with accuracies above previous studies. Beside increased accuracies, the paper shows how such parsimonious measures allows us to generalize our findings to the entire population of a country and even across national borders, to other political multiparty systems. The approach in this study relies on data that are publicly available, and the simple setup we propose can with some limitations, be generalized to millions of users in other multiparty systems.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pone00:0184562
    DOI: 10.1371/journal.pone.0184562
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    References listed on IDEAS

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    1. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    2. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    3. Mavragani, Amaryllis & Tsagarakis, Konstantinos P., 2016. "YES or NO: Predicting the 2015 GReferendum results using Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 109(C), pages 1-5.
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    Cited by:

    1. Cerina, Roberto & Duch, Raymond, 2020. "Measuring public opinion via digital footprints," International Journal of Forecasting, Elsevier, vol. 36(3), pages 987-1002.
    2. 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.
    3. Franziska Marquart & Jakob Ohme & Judith Möller, 2020. "Following Politicians on Social Media: Effects for Political Information, Peer Communication, and Youth Engagement," Media and Communication, Cogitatio Press, vol. 8(2), pages 197-207.

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