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How Chinese Officials Use the Internet to Construct Their Public Image

Published online by Cambridge University Press:  24 July 2017

Jennifer Pan
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Abstract

The Chinese regime has launched a number of online government transparency initiatives to increase the volume of publicly available information about the activities of lower-level governments. By analyzing online content produced by local government officials to fulfill these transparency requirements—a random sample of 1.92 million county-level government web pages—this paper shows how websites are commandeered by local-level officials to construct their public image. The majority of content on government websites emphasizes either the competence or benevolence of county executives, depending on where leaders are in the political tenure cycle. Early tenure county executives project images of benevolence by emphasizing their attentiveness and concern toward citizens. Late tenure executives project images of competence by highlighting their achievements. These findings shift the nature of debates concerning the role of the Internet in authoritarian regimes from a focus on regime–society interactions to an examination of dynamics among regime insiders. By focusing on communication and the flow of information between upper-level leaders and lower-level regime agents, this paper reveals how the Internet becomes a vehicle of self-promotion for local politicians.

Type
Original Articles
Information
Political Science Research and Methods , Volume 7 , Issue 2 , April 2019 , pp. 197 - 213
Copyright
© The European Political Science Association 2017 

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Footnotes

*

Jennifer Pan, Assistant Professor, Department of Communication, Building 120, 450 Serra Mall, Stanford University, Stanford, CA 94305 (jp1@stanford.edu). The author thanks to Chris Adolph, Emily Beaulieu, Sheena Chestnut-Greitens, Jared Chung, Jorge Domingeuz, Adam Glynn, Shelby Grossman, Shanto Iyengar, Kyle Jaros, Gary King, Burt Monroe, Jean Oi, Elizabeth Perry, Amanda Pinkston, Molly Roberts, Arthur Spirling, and Andrew Walder for many helpful comments and suggestions, as well as a number of research associates for their work on this project. The authors is also grateful to the team and for the infrastructure at the Institute for Quantitative Social Science at Harvard University. To view supplementary material for this article, please visit https://doi.org/10.1017/psrm.2017.15

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Dataset

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