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Title: On Predicting Election Results using Twitter and Linked Open Data: The Case of the UK 2010 Election
Authors: Kalampokis, Evangelos
Karamanou, Areti
Tambouris, Efthimios
Tarabanis, Konstantinos
Type: Article
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: Twitter
Social Media
Predictive Analytics
Linked Data
Issue Date: 2017
Source: Journal of Universal Computer Science
Volume: 23
Issue: 3
First Page: 280
Last Page: 303
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.
Other Identifiers: 10.3217/jucs-023-03-0280
Appears in Collections:Department of Applied Informatics
Department of Business Administration

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