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it - Information Technology

Methods and Applications of Informatics and Information Technology

Editor-in-Chief: Conrad, Stefan / Molitor, Paul

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Volume 56, Issue 5


Can electoral popularity be predicted using socially generated big data?

Taha Yasseri / Jonathan Bright
Published Online: 2014-09-30 | DOI: https://doi.org/10.1515/itit-2014-1046


Today, our more-than-ever digital lives leave significant footprints in cyberspace. Large scale collections of these socially generated footprints, often known as big data, could help us to re-investigate different aspects of our social collective behaviour in a quantitative framework. In this contribution we discuss one such possibility: the monitoring and predicting of popularity dynamics of candidates and parties through the analysis of socially generated data on the web during electoral campaigns. Such data offer considerable possibility for improving our awareness of popularity dynamics. However they also suffer from significant drawbacks in terms of representativeness and generalisability. In this paper we discuss potential ways around such problems, suggesting the nature of different political systems and contexts might lend differing levels of predictive power to certain types of data source. We offer an initial exploratory test of these ideas, focussing on two data streams, Wikipedia page views and Google search queries. On the basis of this data, we present popularity dynamics from real case examples of recent elections in three different countries.

Keywords: ACM CCS→Applied computing→Law; social and behavorial sciences→Sociology; ACM CCS→Applied computing→Physical sciences and engineering→Mathematics and statistics; ACM CCS→Human-centered computing→Collaborative and social computing→Computer supported cooperative work; ACM CCS→Networks→Network algorithms→Traffic engineering algorithms

About the article

Taha Yasseri

Taha Yasseri holds a PhD from the institute of theoretical physics at the University of Göttingen, Germany, where he worked on spontaneous pattern formation in complex systems followed by two years of Postdoctoral Research at the Budapest University of Technology and Economics, working on the socio-physical aspects of the community of Wikipedia editors. He is now a researcher at the Oxford Internet Institute, University of Oxford. His main research focus now is on online societies, government-citizen interactions on the web and the structural evolution of the web. He uses mathematical models and data analysis to study social systems quantitatively.

Oxford Internet Institute, University of Oxford, 1 St Giles', OX1 3JS Oxford, UK, Tel.: +44-1865-287229, Fax: +44-1865-287211

Jonathan Bright

Jonathan Bright is a political scientist specialising in computational and “big data” approaches to the social sciences. He holds a BSc in Computer Science from the University of Bristol, an MSc in International Politics from the School of Oriental and African Studies, and a PhD in Political Science from the European University Institute. His major research interests lie in both the quantitative study of online news media (and, more generally, virtual “public spheres”), and also the large scale analysis of politicians and parliamentary behaviour. More generally, he is also interested in developing computational social research methods, and in driving forward computing skills as a core part of social science methodology.

Oxford Internet Institute, University of Oxford, 1 St Giles', OX1 3JS Oxford, UK, Tel.: +44-1865-287233, Fax: +44-1865-287211

Accepted: 2014-09-09

Received: 2014-04-16

Published Online: 2014-09-30

Published in Print: 2014-10-28

Citation Information: it - Information Technology, Volume 56, Issue 5, Pages 246–253, ISSN (Online) 2196-7032, ISSN (Print) 1611-2776, DOI: https://doi.org/10.1515/itit-2014-1046.

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