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How Sudden Censorship Can Increase Access to Information

  • WILLIAM R. HOBBS (a1) and MARGARET E. ROBERTS (a2)
Abstract

Conventional wisdom assumes that increased censorship will strictly decrease access to information. We delineate circumstances when increases in censorship expand access to information for a substantial subset of the population. When governments suddenly impose censorship on previously uncensored information, citizens accustomed to acquiring this information will be incentivized to learn methods of censorship evasion. These evasion tools provide continued access to the newly blocked information—and also extend users’ ability to access information that has long been censored. We illustrate this phenomenon using millions of individual-level actions of social media users in China before and after the block of Instagram. We show that the block inspired millions of Chinese users to acquire virtual private networks, and that these users subsequently joined censored websites like Twitter and Facebook. Despite initially being apolitical, these new users began browsing blocked political pages on Wikipedia, following Chinese political activists on Twitter, and discussing highly politicized topics such as opposition protests in Hong Kong.

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Corresponding author
William R. Hobbs is a Postdoctoral Fellow at the Network Science Institute, Northeastern University, S360 Huntington Avenue, 1010-177, Boston, MA 02115 (w.hobbs@northeastern.edu), https://web.northeastern.edu/whobbs/.
Margaret E. Roberts is an Assistant Professor in the Department of Political Science, University of California, San Diego, Social Sciences Building 301, 9500 Gilman Drive #0521, La Jolla, CA 92093-0521 (meroberts@ucsd.edu), www.MargaretRoberts.net.
Footnotes
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We thank the following people for helpful comments and suggestions on this work: Alexei Abrahams, Eli Berman, Allen Carlson, Dean Eckles, James Fowler, Lei Guang, Navid Hassanpour, Ruixue Jia, Holger Kern, Gary King, Shuhei Kurizaki, Jennifer Pan, Maria Petrova, Susan Shirk, Zachary Steinert-Threlkeld, Brian Tsay, Yiqing Xu, Jason Wu, and David Yang. We specifically thank Zachary Steinert-Threlkeld for graciously supplying geolocated Twitter data from China and Pablo Barberá who helped us access geolocated Instagram data. Thanks to Yingjie Fan for excellent research assistance. We thank joint funding from the Policy Design and Evaluation Lab and Center for Global Transformation at UC San Diego and the Hellman Fellows Fund. Replication files can be found on the American Political Science Review Dataverse: https://doi.org/10.7910/DVN/Q8NRTS.

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