Dina Rosenberg

Associate Professor


Curriculum vitae



Department of Political Science and Economics

Rowan University



How the internet and social media reduce government approval: empirical evidence from Russian regions


Journal article


Dina Rosenberg, Eugenia Tarnikova
Post-Soviet Affairs, 2022

Semantic Scholar DOI
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APA   Click to copy
Rosenberg, D., & Tarnikova, E. (2022). How the internet and social media reduce government approval: empirical evidence from Russian regions. Post-Soviet Affairs.


Chicago/Turabian   Click to copy
Rosenberg, Dina, and Eugenia Tarnikova. “How the Internet and Social Media Reduce Government Approval: Empirical Evidence from Russian Regions.” Post-Soviet Affairs (2022).


MLA   Click to copy
Rosenberg, Dina, and Eugenia Tarnikova. “How the Internet and Social Media Reduce Government Approval: Empirical Evidence from Russian Regions.” Post-Soviet Affairs, 2022.


BibTeX   Click to copy

@article{dina2022a,
  title = {How the internet and social media reduce government approval: empirical evidence from Russian regions},
  year = {2022},
  journal = {Post-Soviet Affairs},
  author = {Rosenberg, Dina and Tarnikova, Eugenia}
}

Abstract

ABSTRACT In this paper we study the effect of the internet and social media on government approval. On the one hand, the internet exposes people to independent information, which makes them possibly more critical of the government. On the other, many countries use the internet for propaganda, which might increase support for the government. We study these effects via the example of Russia. We utilize data from an existing survey: the resulting dataset contains data on 17,824 individual-level observations from 64 regions in Russia, 2010–2019. We find that more intensive internet use and access to social media are associated with a decrease in government approval. Yet, the influence of social media is more nuanced. The Russian-language homegrown network VKontakte increases public approval of the government. To partially account for self-selection bias, we use the propensity score matching method. Our results remain robust and allow us to make causal inferences.


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