  We present measurements and analysis of censorship on Weibo, a popular
microblogging site in China. Since we were limited in the rate at which we
could download posts, we identified users likely to participate in sensitive
topics and recursively followed their social contacts. We also leveraged new
natural language processing techniques to pick out trending topics despite the
use of neologisms, named entities, and informal language usage in Chinese
social media. We found that Weibo dynamically adapts to the changing interests
of its users through multiple layers of filtering. The filtering includes both
retroactively searching posts by keyword or repost links to delete them, and
rejecting posts as they are posted. The trend of sensitive topics is
short-lived, suggesting that the censorship is effective in stopping the
"viral" spread of sensitive issues. We also give evidence that sensitive topics
in Weibo only scarcely propagate beyond a core of sensitive posters.

Information Retrieval;Cryptography and Security