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Recently, two influential PNAS papers have shown how our preferences for'Hello Kitty' and 'Harley Davidson', obtained through Facebook likes, canaccurately predict details about our personality, religiosity, politicalattitude and sexual orientation (Konsinski et al. 2013; Youyou et al 2015). Inthis paper, we make the claim that though the wide variety of Facebook likesmight predict such personal traits, even more accurate and generalizableresults can be reached through applying a contexts-specific, parsimonious datastrategy. We built this claim by predicting present day voter intention basedsolely on likes directed toward posts from political actors. Combining theonline and offline, we join a subsample of surveyed respondents to their publicFacebook activity and apply machine learning classifiers to explore the linkbetween their political liking behaviour and actual voting intention. Throughthis work, we show how even a single well-chosen Facebook like, can reveal asmuch about our political voter intention as hundreds of random likes. Further,by including the entire political like history of the respondents, our modelreaches prediction accuracies above previous multiparty studies (60-70%). Weconclude the paper by discussing how a parsimonious data strategy applied, withsome limitations, allow us to generalize our findings to the 1,4 million Daneswith at least one political like and even to other political multipartysystems.

Parsimonious Data: How a single Facebook like predicts voting behaviour in multiparty systems