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The resource model of political participation 2.0: Protesting in semi-authoritarian regimes – A privilege of the privileged
Linnaeus University, Faculty of Social Sciences, Department of Political Science. (DISA;CSS)ORCID iD: 0000-0002-3708-6333
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: urn:nbn:se:lnu:diva-119892DOI: 10.15626/LUD.484.2023ISBN: 9789180820011 (print)ISBN: 9789180820028 (electronic)OAI: oai:DiVA.org:lnu-119892DiVA, id: diva2:1744951
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
List of papers
1. How the Internet has changed participation: Exploring distinctive preconditions of online activism
Open this publication in new window or tab >>How the Internet has changed participation: Exploring distinctive preconditions of online activism
2021 (English)In: Communication & Society, ISSN 0214-0039, Vol. 34, no 2, p. 67-85Article in journal (Refereed) Published
Abstract [en]

The continuously growing number of people participating in Internet-based, online, political activism suggests that the latter has the potential to replace offline forms of unconventional political participation in the future. If that is the case, it is essential to understand the nature and objectives of such type of participation. This article addresses the question of distinctive preconditions of online activism. As a result of the mixed-effect logistic regression analysis of the European Social Survey data, it was found that online activism contrasts with other unconventional types of political participation in respect to the effect of social trust. It is suggested that the key differences between the preconditions of online and offline forms of participation may speak in favour of several phenomena. First of all, it is proposed that social networking services (SNSs) managed to create an illusion of directness of political participation. Secondly, new groups of people with the lower risk preferences may be recruited into online political action. Lastly, groups that do not believe in the effectiveness of political participation or that have other motives, such as a search for attention, may be more likely to participate online. The results call for further research on how SNSs reshape how people understand political engagement and how they want to be involved.

Keywords
online activism, political participation, political trust, social trust, trust in the political system, trust in political institutions, Civic Voluntarism Model
National Category
Political Science (excluding Public Administration Studies and Globalisation Studies)
Research subject
Social Sciences, Political Science
Identifiers
urn:nbn:se:lnu:diva-102170 (URN)10.15581/003.34.2.67-85 (DOI)000640228500005 ()2-s2.0-85104999282 (Scopus ID)
Available from: 2021-04-15 Created: 2021-04-15 Last updated: 2023-03-21Bibliographically approved
2. Predicting online participation through Bayesian network analysis
Open this publication in new window or tab >>Predicting online participation through Bayesian network analysis
2021 (English)In: PLOS ONE, E-ISSN 1932-6203, Vol. 16, no 12, article id e0261663Article in journal (Refereed) Published
Abstract [en]

Despite the fact that preconditions of political participation were thoroughly examined before, there is still not enough understanding of which factors directly affect political participation and which factors correlate with participation due to common background variables. This article scrutinises the causal relations between the variables associated with participation in online activism and introduces a three-step approach in learning a reliable structure of the participation preconditions’ network to predict political participation. Using Bayesian network analysis and structural equation modeling to stabilise the structure of the causal relations, the analysis showed that only age, political interest, internal political efficacy and no other factors, highlighted by the previous political participation research, have direct effects on participation in online activism. Moreover, the direct effect of political interest is mediated by the indirect effects of internal political efficacy and age via political interest. After fitting the parameters of the Bayesian network dependent on the received structure, it became evident that given prior knowledge of the explanatory factors that proved to be most important in terms of direct effects, the predictive performance of the model increases significantly. Despite this fact, there is still uncertainty when it comes to predicting online participation. This result suggests that there remains a lot to be done in participation research when it comes to identifying and distinguishing factors that stimulate new types of political activities.

Place, publisher, year, edition, pages
Public Library of Science, 2021
Keywords
Bayesian network analysis, structural equation modeling, causality, political participation, online activism, internal political efficacy
National Category
Political Science (excluding Public Administration Studies and Globalisation Studies)
Research subject
Social Sciences, Political Science
Identifiers
urn:nbn:se:lnu:diva-108759 (URN)10.1371/journal.pone.0261663 (DOI)000755252200063 ()2-s2.0-85122018580 (Scopus ID)
Available from: 2022-01-02 Created: 2022-01-02 Last updated: 2023-03-30Bibliographically approved
3. Online mobilisation strategies: Increasing political participation in semi-authoritarian regimes
Open this publication in new window or tab >>Online mobilisation strategies: Increasing political participation in semi-authoritarian regimes
2025 (English)In: Journal of Information Technology & Politics, ISSN 1933-1681, E-ISSN 1933-169X, Vol. 22, no 1, p. 16-31Article in journal (Refereed) Published
Abstract [en]

Although the importance of weaker and no ties for political-information diffusion was established, previous research did not focus on their primary role in mobilization into low-cost political activities. This study examines online mobilization strategies that increase e-petition signing in semi-authoritarian regimes. The research question is approached by assessing mobilization carried out on Twitter, where communication takes place via weaker ties or no ties, and the Russian social networking service VKontakte, characterized by comparatively stronger ties. To examine how the benefits of participation affect the effectiveness of different mobilization steps, participation on the two biggest e-petitioning platforms in Russia, the Russian public initiative and Change.org, is compared. The significance of the information, persuasion and support effects on the petition signing is tested through linear regression and Bayesian network analyses. The results show that irrespective of the benefits of participation, information-sharing is the most important step for online recruitment into low-cost political activities. Meanwhile, persuasion and social support have no direct impact on e-petition signing.

Place, publisher, year, edition, pages
Taylor & Francis, 2025
National Category
Political Science
Research subject
Social Sciences, Political Science
Identifiers
urn:nbn:se:lnu:diva-119890 (URN)10.1080/19331681.2023.2202647 (DOI)000972788600001 ()2-s2.0-85153534515 (Scopus ID)
Available from: 2023-03-21 Created: 2023-03-21 Last updated: 2025-01-15Bibliographically approved
4. Using Social-Media-Network Ties for Predicting Intended Protest Participation in Russia
Open this publication in new window or tab >>Using Social-Media-Network Ties for Predicting Intended Protest Participation in Russia
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
Keywords
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:nbn:se:lnu:diva-119891 (URN)10.1016/j.osnem.2023.100273 (DOI)001279930200002 ()2-s2.0-85174799461 (Scopus ID)
Available from: 2023-03-21 Created: 2023-03-21 Last updated: 2024-08-22Bibliographically approved

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