CNetS Associate Professor Johan Bollen interviewed on CNBC and CNN for his work on sentiment tracking via Twitter

Johan Bollen discussed his findings that the public’s collective mood as expressed on Twitter correlates in a surprising way to the performance of the Dow Jones Industrial Average today on CNBC’s Squawk on the Street and CNN International’s Quest Means Business. Bollen explained that contrary to expectations, the public’s overall anxiety appears to predict DJIA closing values three to four days in advance, rather than following from DJIA performance.

The interview draws from a paper authored by Bollen and colleagues called “Twitter Mood Predicts the Stock Market” wherein the researchers presented their findings after applying the Google-Profile of Mood States (GPOMS) algorithm to 9.7 million tweets between March and December 2008. The algorithm measures public mood by analyzing the textual content of Tweets before indexing that content into one of six mood states. One of the mood states “calmness” was demonstrated to be significantly predictive of market performance.