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

Linguistic Fingerprints of Internet Censorship: the Case of SinaWeibo