TweetCast – What you tweet says a lot about your politics and who you are going to vote for in this highly volatile presidential election.

TweetCast, a new online tool, is able to predict with 80 percent accuracy who will vote for Donald Trump and who will vote for Hillary Clinton.

Try TweetCast. Can it correctly predict who you support?

Perhaps more surprising, the tool can also predict which states will go blue or red (Democrat or Republican).

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Tweeting the words “lying,” “liberal,” “illegal” and “money,” for example, indicates a vote for Trump. Using the words “single,” “humanity,” “rights” and “y’all,” on the other hand, predicts a vote for Clinton.

We use less slang after a hashtag on Twitter

“These are not the most prevalent terms that voters use on Twitter,” says Larry Birnbaum, professor of computer science in Northwestern University’s McCormick School of Engineering. “They are the most predictive terms.”

TweetCast uses a machine-learning algorithm to examine words, hashtags, tagged usernames, and mentioned websites to uncover which terms are most predictive of voting preference.

“TweetCast is a good example of what we can tell about you from Twitter.”

Birnbaum’s team did not develop the algorithm used in TweetCast, but they are the first to apply the approach to determining political preferences by analyzing tweets.

Birnbaum and his students first launched a version of TweetCast for the 2012 presidential election. The algorithm was trained on Twitter users who have publicly declared support for one of the two candidates. During training, the algorithm found patterns in those users’ activity and applied those patterns to users across Twitter.

For this year’s presidential election, Birnbaum and PhD student Jason Cohn expanded the tool to predict the states Trump will take and the states Clinton will take.

By using Twitter’s geo-location feature, the algorithm randomly sampled approximately 80,000 Twitter users from each state. Based on those users’ predictive words, TweetCast could make a prediction for which states will most likely vote blue (New York, California, and Illinois, for example) or red (Mississippi, Arkansas, and Texas).

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TweetCast is still experimental and has encountered some issues. States with fewer Twitter users, such as Wyoming and Montana, are trickier to predict. Also, Twitter users skew young and liberal. Researchers are working with machine-learning expert Douglas Downey, associate professor of computer science, to explore ways to compensate for these biases.

One can imagine how TweetCast’s information can help campaigns target voters and use Twitter to push voter turnout, but it also shows that many preferences can be gleaned from Twitter, Birnbaum says.

“TweetCast is a good example of what we can tell about you from Twitter. We can determine a lot from the language you use, including which restaurants you like, books you read, sports you enjoy, news you consume—and who you’ll vote for.”

Source: Northwestern University