“w00t! feeling great today!” chatter in Twitter: identification and prevalence

Abstract

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.