How Twitter can help predict an election
By Fabio Rojas, WashPost, Published: August 11
Fabio Rojas is an associate professor of sociology at Indiana University. He blogs at orgtheory.net.
Digital democracy is here. We no longer passively watch our leaders on television and register our opinions on Election Day. Modern politics happens when somebody comments on Twitter or links to a campaign through Facebook. In our hyper-networked world, anyone can say anything, and it can be read by millions.
This new world will undermine the polling industry. For nearly a century, conventional wisdom has argued that we can only truly know what the public thinks about an issue if we survey a random sample of adults. An entire industry is built on this view. Nearly every serious political campaign in the United States spends thousands, even millions, of dollars hiring campaign consultants who conduct these polls and interpret the results.
Digital democracy will put these campaign professionals out of work. New research in computer science, sociology and political science shows that data extracted from social media platforms yield accurate measurements of public opinion. It turns out that what people say on Twitter or Facebook is a very good indicator of how they will vote.
How good? In a paper to be presented Monday, co-authors Joseph DiGrazia, Karissa McKelvey, Johan Bollen and I show that Twitter discussions are an unusually good predictor of U.S. House elections. Using a massive archive of billions of randomly sampled tweets stored at Indiana University, we extracted 542,969 tweets that mention a Democratic or Republican candidate for Congress in 2010. For each congressional district, we computed the percentage of tweets that mentioned these candidates. We found a strong correlation between a candidate’s “tweet share” and the final two-party vote share, especially when we account for a district’s economic, racial and gender profile. In the 2010 data, our Twitter data predicted the winner in 404 out of 406 competitive races.
(More here.)
Fabio Rojas is an associate professor of sociology at Indiana University. He blogs at orgtheory.net.
Digital democracy is here. We no longer passively watch our leaders on television and register our opinions on Election Day. Modern politics happens when somebody comments on Twitter or links to a campaign through Facebook. In our hyper-networked world, anyone can say anything, and it can be read by millions.
This new world will undermine the polling industry. For nearly a century, conventional wisdom has argued that we can only truly know what the public thinks about an issue if we survey a random sample of adults. An entire industry is built on this view. Nearly every serious political campaign in the United States spends thousands, even millions, of dollars hiring campaign consultants who conduct these polls and interpret the results.
Digital democracy will put these campaign professionals out of work. New research in computer science, sociology and political science shows that data extracted from social media platforms yield accurate measurements of public opinion. It turns out that what people say on Twitter or Facebook is a very good indicator of how they will vote.
How good? In a paper to be presented Monday, co-authors Joseph DiGrazia, Karissa McKelvey, Johan Bollen and I show that Twitter discussions are an unusually good predictor of U.S. House elections. Using a massive archive of billions of randomly sampled tweets stored at Indiana University, we extracted 542,969 tweets that mention a Democratic or Republican candidate for Congress in 2010. For each congressional district, we computed the percentage of tweets that mentioned these candidates. We found a strong correlation between a candidate’s “tweet share” and the final two-party vote share, especially when we account for a district’s economic, racial and gender profile. In the 2010 data, our Twitter data predicted the winner in 404 out of 406 competitive races.
(More here.)
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