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This study provides preliminary insights into the linguistic features that contribute to Internet censorship in mainland China. We collected a corpus of 344 censored and uncensored microblog posts thatwere published onSinaWeibo and built a Naive Bayes classifier based on the linguistic, topic-independent, features. The classifier achieves a 79.34%accuracy in predicting whether a blog post would be censored on Sina Weibo.
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Linguistic Fingerprints of Internet Censorship: the Case of Sina Weibo
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.
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- Award ID(s):
- 1704113
- PAR ID:
- 10463470
- Date Published:
- Journal Name:
- Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Internet censorship imposes restrictions on what information can be publicized or viewed on the Internet. According to Freedom House’s annual Freedom on the Net report, more than half the world’s Internet users now live in a place where the Internet is censored or restricted. China has built the world’s most extensive and sophisticated online censorship system. In this paper, we describe a new corpus of censored and uncensored social media tweets from a Chinese microblogging website, Sina Weibo, collected by tracking posts that mention ‘sensitive’ topics or authored by ‘sensitive’ users. We use this corpus to build a neural network classifier to predict censorship. Our model performs with a 88.50% accuracy using only linguistic features. We discuss these features in detail and hypothesize that they could potentially be used for censorship circumvention.more » « less
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
