  This paper studies how the linguistic components of blogposts collected from
Sina Weibo, a Chinese microblogging platform, might affect the blogposts'
likelihood of being censored. Our results go along with King et al. (2013)'s
Collective Action Potential (CAP) theory, which states that a blogpost's
potential of causing riot or assembly in real life is the key determinant of it
getting censored. Although there is not a definitive measure of this construct,
the linguistic features that we identify as discriminatory go along with the
CAP theory. We build a classifier that significantly outperforms non-expert
humans in predicting whether a blogpost will be censored. The crowdsourcing
results suggest that while humans tend to see censored blogposts as more
controversial and more likely to trigger action in real life than the
uncensored counterparts, they in general cannot make a better guess than our
model when it comes to `reading the mind' of the censors in deciding whether a
blogpost should be censored. We do not claim that censorship is only determined
by the linguistic features. There are many other factors contributing to
censorship decisions. The focus of the present paper is on the linguistic form
of blogposts. Our work suggests that it is possible to use linguistic
properties of social media posts to automatically predict if they are going to
be censored.
