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Using Social-Media-Network Ties for Predicting Intended Protest Participation in Russia
Linnaeus University, Faculty of Social Sciences, Department of Political Science. (DISA;CSS)ORCID iD: 0000-0002-3708-6333
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). University of Eastern Finland, Finland. (DISA)ORCID iD: 0000-0002-3000-0381
Linköping University, Sweden.ORCID iD: 0000-0002-1907-7820
2023 (English)In: Online Social Networks and Media, E-ISSN 2468-6964, Vol. 37-38, article id 100273Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2023. Vol. 37-38, article id 100273
Keywords [en]
Political participation, Protesting, Machine learning, Russia, Social networks, Social media
National Category
Political Science Computer and Information Sciences
Research subject
Computer and Information Sciences Computer Science; Social Sciences, Political Science
Identifiers
URN: urn:nbn:se:lnu:diva-119891DOI: 10.1016/j.osnem.2023.100273ISI: 001279930200002Scopus ID: 2-s2.0-85174799461OAI: oai:DiVA.org:lnu-119891DiVA, id: diva2:1744914
Available from: 2023-03-21 Created: 2023-03-21 Last updated: 2024-08-22Bibliographically approved
In thesis
1. The resource model of political participation 2.0: Protesting in semi-authoritarian regimes – A privilege of the privileged
Open this publication in new window or tab >>The resource model of political participation 2.0: Protesting in semi-authoritarian regimes – A privilege of the privileged
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Transitioning to and sustaining democracy cannot be possible without active citizens joining protests, distributing political information, or representing fellow citizens in legislative institutions. Due to this, political-science research for several decades has tried to investigate why some citizens are involved in political decision-making while others prefer to refrain from it.

Many scholars have suggested that citizens’ political participation is, at large, explained by their interest in politics and political knowledge. However, in the time of shifting towards the digital era, social media has substantially increased the speed and scope of information sharing and overall political knowledge. Additionally, attention seekers populating social networking sites promote mindfulness, consciousness, pro-activeness, and altruism, popularising online activism, boycotting, buycotting, and protesting. Yet, the scale of protest participation in semi-authoritarian regimes, which have a high potential to democratise, remains limited. If political interest or knowledge cannot really explain why this is the case, what can?

In this dissertation, I tested hypotheses grounded in political-participation, social-capital, political-mobilisation, and rational-choice research traditions, as well as new hypotheses generated by studying the patterns in original data. In this fashion, I sought to find the underlying factors behind limited protest participation in semi-authoritarian regimes. 

By studying what is traditionally referred to as unconventional participation (e.g., online activism, petition-signing, and protesting) in democratic and semi-authoritarian regimes and participation in the Russian Federation as a representative case, I have developed an explanatory model of contemporary political participation. In the Russian context, the model proved to be 96% accurate at predicting protest participation.

Based on the results of this study and those reported by other scholars, I concluded that socioeconomic status (SES) is at the root of inequalities in political participation. While high-SES individuals acquire advantageous social networks that give them access to political information, low-SES individuals are often excluded from political processes altogether. This dissertation demonstrated that individual social networks—and not time, money, or civic skills—are the most critical resource for contemporary participation.

Place, publisher, year, edition, pages
Linnaeus University Press, 2023. p. 71
Series
Linnaeus University Dissertations ; 484
National Category
Political Science Computer and Information Sciences
Research subject
Social Sciences, Political Science
Identifiers
urn:nbn:se:lnu:diva-119892 (URN)10.15626/LUD.484.2023 (DOI)9789180820011 (ISBN)9789180820028 (ISBN)
Public defence
2023-04-21, Weber, Hus K, Växjö, 13:00 (English)
Opponent
Supervisors
Available from: 2023-03-21 Created: 2023-03-21 Last updated: 2024-03-19Bibliographically approved

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Kopacheva, ElizavetaFatemi, MasoudKucher, Kostiantyn

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