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Using Social-Media-...
Using Social-Media-Network Ties for Predicting Intended Protest Participation in Russia
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- Kopacheva, Elizaveta (författare)
- Linnéuniversitetet,Institutionen för statsvetenskap (ST),DISA;CSS,Department of Political Science & Centre for Data Intensive Sciences and Applications (DISA), Linnaeus University
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- Fatemi, Masoud (författare)
- Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM),Centre for Data Intensive Sciences and Applications (DISA), Linnaeus University and School of Computing, University of Eastern Finland
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- Kucher, Kostiantyn, Dr. 1989- (författare)
- Linköpings universitet,Medie- och Informationsteknik,Tekniska fakulteten,iVis, INV
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(creator_code:org_t)
- Elsevier, 2023
- 2023
- Engelska.
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Ingår i: Online Social Networks and Media. - : Elsevier. - 2468-6964. ; 37-38
- Relaterad länk:
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https://doi.org/10.1...
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https://lnu.diva-por... (primary) (Raw object)
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https://liu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- Previous research has highlighted the importance of network structures in information diffusion on social media. In this study, we explore the role of an individual’s social network structure in predicting publicly announced intention of protest participation. Using the case of ecological protests in Russia and applying machine learning to publicly-available VKontakte data, we classify users into protesters and non-protesters. We have found that personal social networks have a high predictive power allowing user classification with an accuracy of 81%. Meanwhile, using all public VKontakte data, including memberships in activist groups and friendship ties to protesters, we were able to classify users into protesters and non-protesters with a higher accuracy of 96%. Our study contributes to the political-participation literature by demonstrating the importance of personal social networks in predicting protest participation. Our results suggest that in some cases, the likelihood of participating in protests can be significantly influenced by elements of a personal-network structure, inter alia, network density and size. Further explanatory research should be done to explore the mechanisms underlying these relationships.
Ämnesord
- SAMHÄLLSVETENSKAP -- Statsvetenskap (hsv//swe)
- SOCIAL SCIENCES -- Political Science (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Political participation
- Protesting
- Machine learning
- Russia
- Social networks
- Social media
- Data- och informationsvetenskap
- Computer and Information Sciences Computer Science
- Statsvetenskap
- Political Science
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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