Title: Parsimonious Data: How a single Facebook like predicts voting behaviour in multiparty systems,
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 intention. Through
this work, we show how even a single well-chosen Facebook like, can reveal as
much about our political voter intention as hundreds of random likes. Further,
by including the entire political like history of the respondents, our model
reaches prediction accuracies above previous multiparty studies (60-70%). We
conclude the paper by discussing how a parsimonious data strategy applied, with
some limitations, allow us to generalize our findings to the 1,4 million Danes
with at least one political like and even to other political multiparty
systems.