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Automated analysis of the US presidential elections using Big Data and network analysis

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journal contribution
posted on 2015-03-04, 17:16 authored by S. Sudhahar, Giuseppe A. Veltri, N. Cristianini
The automated parsing of 130,213 news articles about the 2012 US presidential elections produces a network formed by the key political actors and issues, which were linked by relations of support and opposition. The nodes are formed by noun phrases and links by verbs, directly expressing the action of one node upon the other. This network is studied by applying insights from several theories and techniques, and by combining existing tools in an innovative way, including: graph partitioning, centrality, assortativity, hierarchy and structural balance. The analysis yields various patterns. First, we observe that the fundamental split between the Republican and Democrat camps can be easily detected by network partitioning, which provides a strong validation check of the approach adopted, as well as a sound way to assign actors and topics to one of the two camps. Second, we identify the most central nodes of the political camps. We also learnt that Clinton played a more central role than Biden in the Democrat camp; the overall campaign was much focused on economy and rights; the Republican Party (Grand Old Party or GOP) is the most divisive subject in the campaign, and is portrayed more negatively than the Democrats; and, overall, the media reported positive statements more frequently for the Democrats than the Republicans. This is the first study in which political positions are automatically extracted and derived from a very large corpus of online news, generating a network that goes well beyond traditional word-association networks by means of richer linguistic analysis of texts.

Funding

This work was supported by EU-funded research projects CompLACS (FP7-ICT 270327); and ThinkBig (FP7-IDEAS-ERC 339365).

History

Citation

Big Data & Society, 2015, 2 (1)

Author affiliation

/Organisation/COLLEGE OF SOCIAL SCIENCE/Department of Media and Communication

Version

  • VoR (Version of Record)

Published in

Big Data & Society

Publisher

SAGE Publications Ltd.

eissn

2053-9517

Copyright date

2053

Available date

2015-03-04

Publisher version

http://bds.sagepub.com/content/2/1/2053951715572916

Language

en

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