Twitter's open and public network allows to directly observe how messages are reaching and influencing users by following responses. Twitter provides two forms of response: replies and retweets. Responses thus serve both as a measure of distribution and as a way to increase it. Understanding this dynamic for prediction would be valuable information for any content generator. In this work, we describe methods to predict if a given tweet will elicit a response from the social network once it's posted. To accomplish this task we exploit features derived from various sources of signal such as the language used in the tweet, the social network and the user's history. We use these features and leverage historical data to automatically train prediction models from a stream of real tweets collected over a two weeks period. We empirically show that our models are capable of generating accurate predictions over a subset of the tweets population.
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