An Indiana University study found that the percentage of votes for Republican and Democratic candidates in 2010 and 2012 races for the U.S. House of Representatives could be predicted by the percentage of tweets that mentioned those candidates -- and it didn't matter whether the tweets were positive or negative.
"Think of this as a measurement of buzz," said Fabio Rojas, an associate professor of sociology in the College of Arts and Sciences at IU Bloomington. "We call this the 'all publicity is good publicity' finding. Even if you don't like somebody, you would only talk about them if they're important."
Rojas and colleagues in the Department of Sociology and School of Informatics and Computing analyzed a sample of 537 million tweets to examine whether online social media behavior could be used to assess real world political behavior.
Study lead author Joseph DiGrazia, a doctoral student in the Department of Sociology, will present the findings at the 108th Annual Meeting of the American Sociological Association.
Until now, DiGrazia said, polls and surveys were the primary way to gauge public attitudes.
"Our findings show there is massive, untapped, reliable data out there that can give insights into public opinion," he said.
Unlike other studies that have looked at the influence of social media on election outcomes, their study, "More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior," took into account such variables as incumbency, partisanship, media coverage, and socio-demographic makeup of the electorate.
They say Twitter provides another tool for analyzing races, particularly when other data is in short supply. The study drew from an extensive Twitter database compiled by the Center for Complex Networks and Systems Research at Indiana University's School of Informatics and Computing. The Indiana Twitter database contains the largest sample of tweets in the world that is available to academic researchers.
"With the right planning, someone could monitor races in 2014 on their personal computer," said Karissa McKelvey, a co-author and doctoral student with the Center for Complex Networks and Systems Research.
Co-authors also include Johan Bollen, an associate professor in the School of Informatics and Computing.
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