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In this paper we present a methodology to assess moviegoers' early reactions to movies' premieres through the extraction of analytics from Twitter conversations that take place in the weekend in which a movie is released. We then apply data mining techniques to a sample of 22 movies to identify models able to predict box-office sales in the first weekend. We show that better predictions are obtained when traffic metrics are combined with social network or conversational indicators rather than with sentiment and that online sentiment achieves the lowest explanatory power among all the considered variables. Our findings confirm that the importance of commonly used buzz-metrics, such as sentiment, is probably overstated, and that conversational analytics can contribute significantly to explain the variance of box office revenues in the first week end of release. More broadly, our work adds to research on information diffusion in online networks by providing evidence that diffusion of messages is not content-neutral and that the analysis of conversational dynamics can help to understand the interplay between collective generation and diffusion of content in social networks as well as to obtain insights on whether information diffusion influences off-line behavior. [ABSTRACT FROM AUTHOR]

In this paper we present a novel method to extract and visualize actionable information from streams of social media messages, analyzed as conversational elements. Our method has been applied to over 4 million messages related to more than 35 different events, demonstrating good results identifying conversational patterns. [ABSTRACT FROM AUTHOR]