Reverse Engineering Chinese Censorship through Randomized Experimentation and...
gking.harvard.edu/files/gking/files/experiment_0.pdf
Abstract
Chinese government censorship of social media constitutes the largest coordinated
selective suppression of human communication in recorded history. Although exist-
ing research on the subject has revealed a great deal, it is based on passive, observa-
tional methods, with well known inferential limitations. For example, these methods
can reveal nothing about censorship that occurs before submissions are posted, such
as via automated review which we show is used at two-thirds of all social media sites.
We offer two approaches to overcome these limitations. For causal inferences, we
conduct the first large scale experimental study of censorship 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 com-
puters all over the world which types are censored. Then, for descriptive inferences,
we supplement the current uncertain practice of conducting anonymous interviews
with secret informants, by participant observation: we set up our own social media
site in China, contract with Chinese firms to install the same censoring technologies
as their existing sites, and — with direct access to their software, documentation, and
even customer service help desk support — reverse engineer how it all works. Our
results offer the first rigorous experimental support for the recent hypothesis that crit-
icism of the state, its leaders, and their policies are routinely published, whereas posts
about real world events with collective action potential are censored. We also extend
the hypothesis by showing that it applies even to accusations of corruption by high-
level officials and massive online-only protests, neither of which are censored. We
also reveal for the first time the inner workings of the process of automated review,
and as a result are able to reconcile conflicting accounts of keyword-based content
filtering in the academic literature. We show that the Chinese government tolerates
surprising levels of diversity in automated review technology, but still ensures a uni-
form outcome by post hoc censorship using huge numbers of human coders 符合我的印象，乱说可以，不能组织行动。
页:
[1]