Wikipedia traffic data and electoral prediction: towards theoretically informed models
Oxford Internet Institute, University of Oxford, 1 St Giles’, Oxford, OX1 3JS, UK
* e-mail: firstname.lastname@example.org
Accepted: 5 June 2016
Published online: 18 June 2016
This aim of this article is to explore the potential use of Wikipedia page view data for predicting electoral results. Responding to previous critiques of work using socially generated data to predict elections, which have argued that these predictions take place without any understanding of the mechanism which enables them, we first develop a theoretical model which highlights why people might seek information online at election time, and how this activity might relate to overall electoral outcomes, focussing especially on information seeking incentives related to swing voters and new parties. We test this model on a novel dataset drawn from a variety of countries in the 2009 and 2014 European Parliament elections. We show that while Wikipedia offers little insight into absolute vote outcomes, it does offer good information about changes in overall turnout at elections and about changes in vote share for particular parties. These results are used 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.
Key words: social data / elections / prediction / big data / Wikipedia / public opinion
© Yasseri and Bright, 2016