If you can’t beat China’s censors, why not join them?
To get inside the system, professor Gary King and two Ph.D. students started their own fake social network over the past year, which—while it never formally went online—allowed them to reach out to some of China’s many companies offering censorship software.
Thanks to their software acquisition—purchased from a company that Mr. King declined to name—the Harvard team found a diverse array of tools at their disposal, which allowed them to screen and delete posts according to different keywords and categories, as well as block posts based on user, length of post or time of day.
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Report Discussed in WSJ Story
Margaret E. Roberts
Chinese government censorship of social media constitutes the largest selective suppression of human communication in the history of the world. Although existing systematic research on the subject has revealed a great deal, it is based on passive, observational methods, with well known inferential limitations. We attempt to generate more robust causal and descriptive inferences through participation and experimentation. For causal inferences, we conduct a large scale randomized experimental study by creating accounts on numerous social media sites spread throughout the country, submitting different randomly assigned types of social media texts, and detecting from a network of computers all over the world which types are censored. Then, for descriptive inferences, we supplement the current approach of confidential interviews by setting up our own social media site in China, contracting with Chinese firms to install the same censoring technologies as existing sites, and reverse engineering how it all works. Our results offer unambiguous support for, and clarification of, the emerging view that criticism of the state, its leaders, and their policies are routinely published whereas posts with collective action potential are much more likely to be censored. We are also able to clarify the internal mechanisms of the Chinese censorship apparatus and show that local social media sites have far more flexibility than was previously understood in how (but not what) they censor.
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