The Journal of Machine Learning Research
HHMM-based Chinese lexical analyzer ICTCLAS
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
Emerging topic detection on Twitter based on temporal and social terms evaluation
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Detecting spammers on social networks
Proceedings of the 26th Annual Computer Security Applications Conference
Analyzing the dynamic evolution of hashtags on Twitter: a language-based approach
LSM '11 Proceedings of the Workshop on Languages in Social Media
Suspended accounts in retrospect: an analysis of twitter spam
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
Twitter Trending Topic Classification
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities
Proceedings of the fifth ACM international conference on Web search and data mining
(How) will the revolution be retweeted?: information diffusion and the 2011 Egyptian uprising
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Will this #hashtag be popular tomorrow?
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Predicting emerging social conventions in online social networks
Proceedings of the 21st ACM international conference on Information and knowledge management
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In recent years, social media has risen to prominence in China, with sites like Sina Weibo and Renren each boasting hundreds of millions of users. Social media in China plays a profound role as a platform for breaking news and political commentary that is not available in the state-sanctioned news media. However, like all websites in China, Chinese social media is subject to censorship. Although several studies have identified censorship on Weibo and Chinese blogs, to date no studies have examined the overall impact of censorship on discourse in social media. In this study, we examine how censorship impacts discussions on Weibo, and how users adapt to avoid censorship. We gather tweets and comments from 280K politically active Weibo users for 44 days and use NLP techniques to identify trending topics. We observe that the magnitude of censorship varies dramatically across topics, with 82% of tweets in some topics being censored. However, we find that censorship of a topic correlates with high user engagement, suggesting that censorship does not stifle discussion of sensitive topics. Furthermore, we find that users adopt variants of words (known as morphs) to avoid keyword-based censorship. We analyze emergent morphs to learn how they are adopted and spread by the Weibo user community.