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Predicting online participation through Bayesian network analysis

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...

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Autor principal: Kopacheva, Elizaveta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8699968/
https://www.ncbi.nlm.nih.gov/pubmed/34941953
http://dx.doi.org/10.1371/journal.pone.0261663
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author Kopacheva, Elizaveta
author_facet Kopacheva, Elizaveta
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description 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.
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spelling pubmed-86999682021-12-24 Predicting online participation through Bayesian network analysis Kopacheva, Elizaveta PLoS One Research Article 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. Public Library of Science 2021-12-23 /pmc/articles/PMC8699968/ /pubmed/34941953 http://dx.doi.org/10.1371/journal.pone.0261663 Text en © 2021 Elizaveta Kopacheva https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Kopacheva, Elizaveta
Predicting online participation through Bayesian network analysis
title Predicting online participation through Bayesian network analysis
title_full Predicting online participation through Bayesian network analysis
title_fullStr Predicting online participation through Bayesian network analysis
title_full_unstemmed Predicting online participation through Bayesian network analysis
title_short Predicting online participation through Bayesian network analysis
title_sort predicting online participation through bayesian network analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8699968/
https://www.ncbi.nlm.nih.gov/pubmed/34941953
http://dx.doi.org/10.1371/journal.pone.0261663
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