Detecting censorable content on sina weiboA pilot study
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 that were published on Sina Weibo 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.
MSU Digital Commons Citation
Ng, Kei Yin; Feldman, Anna; and Leberknight, Christopher, "Detecting censorable content on sina weiboA pilot study" (2018). Department of Linguistics Faculty Scholarship and Creative Works. 26.