文章基本信息

标题：On Predicting Election Results using Twitter and Linked Open Data: The Case of the UK 2010 Election
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作者：Evangelos Kalampokis ; Areti Karamanou ; Efthimios Tambouris 等
期刊名称：Journal of Universal Computer Science
印刷版ISSN：0948-6968
出版年度：2017
卷号：23
期号：3
页码：280-303
出版社：Graz University of Technology and Know-Center
摘要：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.
关键词：Twitter; election; linked data; predictive analytics; social media