Parsimonious Data: How a single Facebook like predicts voting behaviour in multiparty systems
Jakob Baek Kristensen, Thomas Albrechtsen, Emil Dahlgaard, Michael, Jensen, Magnus Skovrind, Tobias Bornakke

TL;DR
This study demonstrates that a single Facebook like on political content can accurately predict voting intentions in multiparty systems, outperforming models using broader data sets.
Contribution
It introduces a parsimonious data approach, showing that minimal, context-specific Facebook likes can effectively predict voter behavior in multiparty systems.
Findings
Single political like predicts voting intention as well as multiple likes.
Model achieves 60-70% prediction accuracy, surpassing previous studies.
Findings generalize to large populations in multiparty democracies.
Abstract
Recently, two influential PNAS papers have shown how our preferences for 'Hello Kitty' and 'Harley Davidson', obtained through Facebook likes, can accurately predict details about our personality, religiosity, political attitude and sexual orientation (Konsinski et al. 2013; Youyou et al 2015). In this paper, we make the claim that though the wide variety of Facebook likes might predict such personal traits, even more accurate and generalizable results can be reached through applying a contexts-specific, parsimonious data strategy. We built this claim by predicting present day voter intention based solely on likes directed toward posts from political actors. Combining the online and offline, we join 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…
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