On Predicting Election Results using Twitter and Linked Open Data: The Case of the UK 2010 Election
Evangelos Kalampokis (University of Macedonia, Greece)
Areti Karamanou (University of Macedonia, Greece)
Efthimios Tambouris (University of Macedonia, Greece)
Konstantinos Tarabanis (University of Macedonia, Greece)
Abstract: The analysis of Social Media data enables eliciting public behaviour and opinion. In this context, a number of studies have recently explored Social Media's capability to predict the outcome of real-world phenomena. The results of these studies are controversial with elections being the most disputable phenomenon. The objective of this paper is to present a case of predicting the results of the UK 2010 through Twitter. In particular, we study to what extend it is possible to use Twitter data to accurately predict the percentage of votes of the three most prominent political parties namely the Conservative Party, Liberal Democrats, and the Labour Party. The approach we follow capitalises on (a) a theoretical Social Media data analysis framework for predictions and (b) Linked Open Data to enrich Twitter data. We extensively discuss each step of the framework to emphasise on the details that could affect the prediction accuracy.We anticipate that this paper will contribute to the ongoing discussion of understanding to what extend and under which circumstances election results are predictable through Social Media.
Keywords: Twitter, election, linked data, predictive analytics, social media
Categories: H.3.3, I.2.2, I.2.7, L.3.2