Explaining protest participation in semi-authoritarian regimes: The power of social networks

Previously, the research traditions of social capital and political participation often ran in parallel. This study emphasizes the need to leverage on the advances in social-capital research when studying political participation. Combining the knowledge of two theoretical traditions, I study the effect of mobilization via social networks into protest participation. In comparison to previous research on the effect of mobilization into political participation and the moderating effect of social networks (this research often relied on survey data), this study uses social-networking-site data to reconstruct a network of friendships between informed users. The empirical case analysed in this study is participation in ecological protests in Russia: in particular, I use the case of participation in Shiyes protests (protests against the construction of the largest landfill in northern Russia) and the role of mobilization in VKontakte (VK is the most popular SNS in Russia) in explaining the scale of this participation. Previously, my colleagues and I found that using the data on VK friendships, participation in Shiyes protests can be predicted with the accuracy 96%. In this study, I focus on explanatory rather than predictive potential of the social network. Based on the social network of friendships between informed VK users, I extract information about node centrality; node brokerage and exposure to hierarchy; density, transitivity and size of given ego networks. Based on the previous research on social capital (e.g., Inglehart, 1999; Paxton,1999; Burt, 2001), brokerage and centrality of a given node are expected to improve the strength of mobilization and the likelihood of participation. Network closure and high network density are expected to decrease the scale and quality of mobilization as well as the likelihood of participation. By applying Bayesian structural equation modelling and using the information about individual social networks of the users, I measure the explanatory power of social networks when it comes to protest participation. The results of the study show both size and topology of individual social networks to have direct and indirect (via mobilization) effects on participation. Moreover, the model fitted into the data has high explanatory power, which highlights the importance of individual-social-network structure in explaining protest participation.