Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKalampokis, Evangelos-
dc.contributor.authorKaramanou, Areti-
dc.contributor.authorTambouris, Efthimios-
dc.contributor.authorTarabanis, Konstantinos-
dc.description.abstractThe 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.en_US
dc.sourceJournal of Universal Computer Scienceen_US
dc.subjectFRASCATI::Natural sciences::Computer and information sciencesen_US
dc.subject.otherSocial Mediaen_US
dc.subject.otherPredictive Analyticsen_US
dc.subject.otherLinked Dataen_US
dc.titleOn Predicting Election Results using Twitter and Linked Open Data: The Case of the UK 2010 Electionen_US
dc.contributor.departmentΤμήμα Εφαρμοσμένης Πληροφορικήςen_US
Appears in Collections:Department of Applied Informatics
Department of Business Administration

Files in This Item:
File Description SizeFormat 
jucs_2017.pdf683,43 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.