Muchlinski, D., Yang, X., Birch, S., Macdonald, C. and Ounis, I. (2021) We need to go deeper: measuring electoral violence using convolutional neural networks and social media. Political Science Research and Methods, 9(1), pp. 122-139. (doi: 10.1017/psrm.2020.32)
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Abstract
Electoral violence is conceived of as violence that occurs contemporaneously with elections, and as violence that would not have occurred in the absence of an election. While measuring the temporal aspect of this phenomenon is straightforward, measuring whether occurrences of violence are truly related to elections is more difficult. Using machine learning, we measure electoral violence across three elections using disaggregated reporting in social media. We demonstrate that our methodology is more than 30 percent more accurate in measuring electoral violence than previously utilized models. Additionally, we show that our measures of electoral violence conform to theoretical expectations of this conflict more so than those that exist in event datasets commonly utilized to measure electoral violence including ACLED, ICEWS, and SCAD. Finally, we demonstrate the validity of our data by developing a qualitative coding ontology.
Item Type: | Articles |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Birch, Professor Sarah and Macdonald, Professor Craig and Yang, Dr Xiao and Ounis, Professor Iadh |
Authors: | Muchlinski, D., Yang, X., Birch, S., Macdonald, C., and Ounis, I. |
College/School: | College of Science and Engineering > School of Computing Science College of Social Sciences > School of Social and Political Sciences > Politics |
Journal Name: | Political Science Research and Methods |
Publisher: | Cambridge University Press |
ISSN: | 2049-8470 |
ISSN (Online): | 2049-8489 |
Published Online: | 26 August 2020 |
Copyright Holders: | Copyright © 2020 The European Political Science Association |
First Published: | First published in Political Science Research and Methods 9(1): 122-139 |
Publisher Policy: | Reproduced under a Creative Commons License |
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