Tweeting Under Pressure: Analyzing Trending Topics and Evolving Word Choice on Sina Weibo

Censorship on Weibo does not produce a measurable chilling effect on discussion: Spearman's ρ = 0.198 (p = 0.011) between the percentage of censored tweets and unique tweeters per topic, indicating that censored topics attract more unique participants. No significant negative correlation was found for any of five engagement variables (comments per tweet, comments per user, total comments, unique commentors, unique tweeters).

§5.2, Table 3 evaluation keyword-filtering cn

Comments on Weibo (~18M per day) are not independently censored: when a tweet is deleted, its comments are deleted as a cascade, but no instances of standalone comment censorship were observed in 36.5M tweets and associated comments. This creates a structural asymmetry — there are an order of magnitude more comments than tweets, yet comments persist unless their parent tweet is removed.

§2.3, §4.2 detection keyword-filtering cn

Weibo users circumvent keyword-based censorship by substituting censored terms with morphs — abbreviations, anglicizations, homophones, homographs, and neologisms. 11 of 37 trending topics in a 44-day crawl of 280K users contained morphs, and morph usage was concentrated in heavily censored topics, with up to 5 morphs per topic observed.

§2.3, §6.1 defense keyword-filtering cn

Morph adoption in censored topics begins within hours of censorship being imposed, and in some topics users adopt morphs preemptively before censorship is applied, demonstrating rapid community-level awareness of keyword filtering. Temporal analysis of the Lushan and Taxi topics (Figures 19–20) shows morph usage rising sharply in parallel with or ahead of censor action.

§6, Figures 19–20 evaluation keyword-filtering cn

Weibo employs keyword-based censorship with highly uneven application across topics: 82% of tweets in the Lushan topic (criticism of a local official) were censored, while 27 of 37 trending topics exhibited <2% censorship; overall ~1% of the 36.5M crawled tweets were censored. The Chinese government prioritizes censoring content that could incite public protest over content that is merely critical.

§5.2, Table 1 detection keyword-filtering cn