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Predicting the Future With Social Media
Sitaram Asur
Social Computing Lab
HP Labs
Palo Alto, California
Email: sitaram.asur@hp.com
Bernardo A. Huberman
Social Computing Lab
HP Labs
Palo Alto, California
Email: bernardo.huberman@hp.com
Abstract—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.
I. INTRODUCTION
Social media has exploded as a category of online discourse
where people create content, share it, bookmark it and network
at a prodigious rate. Examples include Facebook, MySpace,
Digg, Twitter and JISC listservs on the academic side. Because
of its ease of use, speed and reach, social media is fast
changing the public discourse in society and setting trends
and agendas in topics that range from the environment and
politics to technology and the entertainment industry.
Since social media can also be construed as a form of
collective wisdom, we decided to investigate its power at
predicting real-world outcomes. Surprisingly, we discovered
that the chatter of a community can indeed be used to make
quantitative predictions that outperform those of artificial
markets. These information markets generally involve the
trading of state-contingent securities, and if large enough and
properly designed, they are usually more accurate than other
techniques for extracting diffuse information, such as surveys
and opinions polls. Specifically, the prices in these markets
have been shown to have strong correlations with