The Journal of Machine Learning Research
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Beyond Microblogging: Conversation and Collaboration via Twitter
HICSS '09 Proceedings of the 42nd Hawaii International Conference on System Sciences
Twitter power: Tweets as electronic word of mouth
Journal of the American Society for Information Science and Technology
Is it really about me?: message content in social awareness streams
Proceedings of the 2010 ACM conference on Computer supported cooperative work
What do people ask their social networks, and why?: a survey study of status message q&a behavior
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Short text classification in twitter to improve information filtering
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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Microblogging services like Twitter are used for a wide variety of purposes and in different modes. Here, we focus on the usage of Twitter for "chatter" i.e., the production and consumption of tweets that are typically non-topical and contain personal status updates or conversational messages which are usually intended and are useful only to the immediate network of the producers of the tweets. The automatic identification of chatter tweets is critical for tasks such as ranking tweets by relevance, matching tweets to advertisements, creation of topical digests of tweets, etc. and generally improves the utility of tweets to people outside the producers' immediate network by enabling the filtering out of tweets that are not of wider interest. We study the prevalence of chatter tweets in Twitter and present techniques to detect them using machine learning techniques that require minimal supervision.