Prediction markets such as InTrade have proven to be a valuable source of insights, if not outright predictions, on the outcomes of everything from recessions to political elections.

Now, researchers have tapped into another vein of insights: social networks themselves. The Social Computing Lab at HP Labs in Palo Alto, California released a report that demonstrates that Twitter can be used as a gauge to determine the success or failure of impending movie releases.

The HP researchers, Sitaram Asur and Bernardo Huberman, scanned three million tweets in a given period of time to first, measure the amount of chatter for a given movie, then ran sentiment analysis, employing the services of  human workers via Amazon Mechanical Turk .

The results tracked closely to actual movie box office revenues. For example, the authors observed, “The movie with the enormous increase in positive sentiment after release is The Blind Side (5.02 to 9.65). The movie had a lukewarm opening weekend sales (34M) but then boomed in the next week (40.1M), owing largely to positive sentiment.”

As Asur and Huberman put it in their conclusion, there’s a lot of potential for a wide range of business and political applications:

“While in this study we focused on the problem of predicting box office revenues of movies for the sake of having a clear metric of comparison with other methods, this method can be extended to a large panoply of topics, ranging from the future rating of products to agenda setting and election outcomes. At a deeper level, this work shows how social media expresses a collective wisdom which, when properly tapped, can yield an extremely powerful and accurate indicator of future outcomes.”

Analyzing Twitter or other social network chatter may be a valuable tool for predicting the success of product launches, new market entrees, and upcoming trends. This study is a great first step, but more studies — as well as actual experience — is needed to gauge how accurate social networking predictions may be. Perhaps it can be thought of as a giant focus group.