Detecting Censorable Content on Sina Weibo: A Pilot Study

Information Gain feature selection from 408 candidates identified informal language markers (informal, nonflu, swear), Chinese modal and general particles signaling mood and relational framing, and physical-feeling words used metaphorically as the top predictors of censored Weibo content — all with statistically significant differences between censored and uncensored classes.

§5, §6 detection keyword-filteringml-classifier cn

A Naive Bayes classifier built on 17 LIWC-derived and keyword features achieved 79.34% accuracy (10-fold cross-validation) predicting censorship of Sina Weibo posts, with precision 0.80 and recall 0.85 for the censored class — outperforming all single-domain feature sets including the full 408-feature combination (0.69 accuracy).

§5, Table 1 detection keyword-filteringml-classifier cn

A 598-term sensitive-keyword blacklist (sourced from Wikipedia and China Digital Times) achieved only 53% classification accuracy on Weibo censorship — below the 66% achieved by punctuation features alone — and appeared in only 31 of 152 uncensored posts versus 60 of 192 censored posts, confirming keywords are not the primary driver of platform censorship decisions.

§4.2, §6, Table 1 detection keyword-filtering cn

Sentiment analysis features (Baidu and Boson tools) achieved only 57% accuracy individually; censored posts averaged 53.9% negative sentiment (General model) versus 49.3% for uncensored — a difference too small to be operationally useful — indicating that sentiment polarity does not reliably distinguish censorable content from permitted content on Weibo.

§4.3–4.5, §6, Table 1 detection keyword-filteringml-classifier cn