Jonathan and I recently published a paper titled “Wikipedia traffic data and electoral prediction: towards theoretically informed models“ in EPJ Data Science.
In this article we examine the possibility of predicting election results by analysing Wikipedia traffic going to different articles related to the parties involved in the election.
Unlike similar work in which socially generated online data is used in an automated learning system to predict the electoral results, without much understanding of mechanisms, here we try to provide a theoretical understanding of voters’ information seeking behaviour around election time and use that understanding to make predictions.
Left panel shows the normalized daily views of the article on the European Parliament Election, 2009 in different langue editions of Wikipedia. The right panel shows the relative change between 2009 and 2014 election turnout in each country vs the relative change in the page view counts of the election article in the corresponding Wikipedia language edition. Germany and Czech Republic are marked as outliers from the general trend.
We test our model on a variety of countries in the 2009 and 2014 European Parliament elections. We show that Wikipedia offers good information about changes in overall turnout at elections and also about changes in vote share for parties. It gives a particularly strong signal for new parties which are emerging to prominence.
We use these results to enhance existing theories about the drivers of aggregate patterns in online information seeking, by suggesting that:
voters are cognitive misers who seek information only when considering changing their vote.
This shows the importance of informal online information in forming the opinions of swing voters, and emphasizes the need for serious consideration of the potentials of systems like Wikipedia by parties, campaign organizers, and institutions which regulate elections.
Read more here.
