lnu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Predicting online participation through Bayesian network analysis
Linnaeus University, Faculty of Social Sciences, Department of Political Science. (Varieties of Political Representation;DISA;CSS)ORCID iD: 0000-0002-3708-6333
2021 (English)In: PLOS ONE, E-ISSN 1932-6203, Vol. 16, no 12, article id e0261663Article in journal (Refereed) Published
Sustainable development
Not refering to any SDG
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. Vol. 16, no 12, article id e0261663
Keywords [en]
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: urn:nbn:se:lnu:diva-108759DOI: 10.1371/journal.pone.0261663ISI: 000755252200063Scopus ID: 2-s2.0-85122018580OAI: oai:DiVA.org:lnu-108759DiVA, id: diva2:1623944
Available from: 2022-01-02 Created: 2022-01-02 Last updated: 2023-03-30Bibliographically 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

Open Access in DiVA

Predicting online participation through Bayesian network analysis(3672 kB)181 downloads
File information
File name DATASET01.pdfFile size 3672 kBChecksum SHA-512
0d9551388f2fcae3b1dcf0ec869d942e895ce0b061a83070beb3e31c3f92ef428c90ab1670db4e294522c123a4470c0f9ab075db168d2657f5f09b8653ab2926
Type datasetMimetype application/pdf

Other links

Publisher's full textScopus

Authority records

Kopacheva, Elizaveta

Search in DiVA

By author/editor
Kopacheva, Elizaveta
By organisation
Department of Political Science
In the same journal
PLOS ONE
Political Science (excluding Public Administration Studies and Globalisation Studies)

Search outside of DiVA

GoogleGoogle Scholar
Total: 0 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 251 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf