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Political homophily in independence movements : analysing and classifying social media users by national identity
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Zubiaga, Arkaitz, Wang, Bo, Liakata, Maria and Procter, Rob (2019) Political homophily in independence movements : analysing and classifying social media users by national identity. IEEE Intelligent Systems , 34 (6). pp. 34-42. doi:10.1109/MIS.2019.2958393
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Official URL: https://doi.org/10.1109/MIS.2019.2958393
Abstract
Social media and data mining are increasingly being used to analyze political and societal issues. Here, we undertake the classification of social media users as supporting or opposing ongoing independence movements in their territories. Independence movements occur in territories whose citizens have conflicting national identities; users with opposing national identities will then support or oppose the sense of being part of an independent nation that differs from the officially recognized country. We describe a methodology that relies on users' self-reported location to build large-scale datasets for three territories-Catalonia, the Basque Country, and Scotland. An analysis of these datasets shows that homophily plays an important role in determining who people connect with, as users predominantly choose to follow and interact with others from the same national identity. We show that a classifier relying on users' follow networks can achieve accurate, language-independent classification performances ranging from 85% to 97% for the three territories.
| Item Type: | Journal Article | |||||||||
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| Subjects: | H Social Sciences > HM Sociology Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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| Divisions: | Faculty of Science > Computer Science | |||||||||
| Library of Congress Subject Headings (LCSH): | Social media , National characteristics , Data mining , Political participation , Social media -- Political aspects | |||||||||
| Journal or Publication Title: | IEEE Intelligent Systems | |||||||||
| Publisher: | IEEE | |||||||||
| ISSN: | 1541-1672 | |||||||||
| Official Date: | 9 December 2019 | |||||||||
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| Volume: | 34 | |||||||||
| Number: | 6 | |||||||||
| Page Range: | pp. 34-42 | |||||||||
| DOI: | 10.1109/MIS.2019.2958393 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Publisher Statement: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
| RIOXX Funder/Project Grant: |
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