  In recent years, social media has become ubiquitous and important for social
networking and content sharing. And yet, the content that is generated from
these websites remains largely untapped. In this paper, we demonstrate how
social media content can be used to predict real-world outcomes. In particular,
we use the chatter from Twitter.com to forecast box-office revenues for movies.
We show that a simple model built from the rate at which tweets are created
about particular topics can outperform market-based predictors. We further
demonstrate how sentiments extracted from Twitter can be further utilized to
improve the forecasting power of social media.
