a1 Harvard University
a2 Harvard University
a3 Harvard University
We offer the first large scale, multiple source analysis of the outcome of what may be the most extensive effort to selectively censor human expression ever implemented. To do this, we have devised a system to locate, download, and analyze the content of millions of social media posts originating from nearly 1,400 different social media services all over China before the Chinese government is able to find, evaluate, and censor (i.e., remove from the Internet) the subset they deem objectionable. Using modern computer-assisted text analytic methods that we adapt to and validate in the Chinese language, we compare the substantive content of posts censored to those not censored over time in each of 85 topic areas. Contrary to previous understandings, posts with negative, even vitriolic, criticism of the state, its leaders, and its policies are not more likely to be censored. Instead, we show that the censorship program is aimed at curtailing collective action by silencing comments that represent, reinforce, or spur social mobilization, regardless of content. Censorship is oriented toward attempting to forestall collective activities that are occurring now or may occur in the future—and, as such, seem to clearly expose government intent.
c1 Gary King is Albert J. Weatherhead III University Professor, Institute for Quantitative Social Science, 1737 Cambridge Street, Harvard University, Cambridge MA 02138 (http://GKing.harvard.edu, firstname.lastname@example.org) (617) 500-7570.
c2 Jennifer Pan is Ph.D. Candidate, Department of Government, 1737 Cambridge Street, Harvard University, Cambridge MA 02138 (http://people.fas.harvard.edu/~jjpan/) (917) 740-5726.
c3 Margaret E. Roberts is Ph.D. Candidate, Department of Government, 1737 Cambridge Street, Harvard University, Cambridge MA 02138 (http://scholar.harvard.edu/mroberts/home).
Our thanks to Peter Bol, John Carey, Justin Grimmer, Navid Hassanpour, Iain Johnston, Bill Kirby, Peter Lorentzen, Jean Oi, Liz Perry, Bob Putnam, Susan Shirk, Noah Smith, Lynn Vavreck, Andy Walder, Barry Weingast, and Chen Xi for many helpful comments and suggestions, and to our insightful and indefatigable undergraduate research associates, Wanxin Cheng, Jennifer Sun, Hannah Waight, Yifan Wu, and Min Yu, for much help along the way. Our thanks also to Larry Summers and John Deutch for helping us to ensure that we satisfy the sometimes competing goals of national security and academic freedom. For help with a wide array of data and technical issues, we are especially grateful to the incredible teams, and for the unparalleled infrastructure, at Crimson Hexagon (CrimsonHexagon.com) and the Institute for Quantitative Social Science at Harvard University (iq.harvard.edu).