Carnegie Mellon University researchers demonstrated how Tweets (“Twitter data” as they call them) replicate opinion estimation for two major U.S. indicators, with some correlations “as high as 80%”, suggesting that readily available texts from social sites could complement and even replace organic polls in the future.
In a study called From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series, researchers found that online social conversation analysis could also “capture important large-scale trends.”
The aim was to compare the reflection of a) consumer confidence and b) political opinion — namely presidential job approval — in 2008 and 2009 as provided by, on the one hand, Twitter messages, and on the other hand, the traditional opinion measurement polls.
Methodology
What the researchers did was collect 1 billion Tweets through Twitter’s API as well as the real-time flow, between 2008 and 2009. They estimate the number of daily messages at 100,000 to 7 million, bearing in mind that the main reason for the variation is Twitter’s own growth.
The “organic” polls that were used to compare consumer confidence were the Index of Consumer Sentiment (ICS) from the Reuters/University of Michigan Surveys of Consumers and the Gallup Organization’s “Economic Confidence” index.
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