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Biden administration moves to protect Alaska wilderness; opening statements and first witness in NY trial; SCOTUS hears Starbucks case, with implications for unions on the line; rural North Carolina town gets pathway to home ownership.

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The Supreme Court weighs cities ability to manage a growing homelessness crisis, anti-Israeli protests spread to college campuses nationwide, and more states consider legislation to ban firearms at voting sites and ballot drop boxes.

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Wyoming needs more educators who can teach kids trade skills, a proposal to open 40-thousand acres of an Ohio forest to fracking has environmental advocates alarmed and rural communities lure bicyclists with state-of-the-art bike trail systems.

Using Tweets to Measure Which Way Voters are Leaning

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Tuesday, November 8, 2016   

CONCORD, N.H. – Most have had their fill of polls, as Granite Staters cast their ballots today, but now there is a new way to measure voter attitudes by studying tweets. Computer scientists from the University of Utah have developed what they call "sentiment analysis" software that can determine how someone feels based on what they write or say. In other words, Americans have learned a lot about how Donald Trump feels about people through Twitter, but how do Twitter users feel about Donald Trump?

Feifei Li, an associate professor at the University of Utah helped develop the program, and said it provides a real-time window into how the public is reacting to political events.

"What's cool is that you can actually adjust the lens of the window," he said. "If you look at the last few months of data altogether, the sentiment for Democrats is stronger than the sentiment for Republicans."

Researchers said the biggest surge of positive tweets for Republicans came during their national convention, and again after the video was released of Trump boasting about sexually assaulting women. The peak for both positive and negative tweets for Democrats came on the heels of the final debates.

Li's team started with more than 250 million global tweets and used advanced software to identify 1.6 million political posts in the United States. Li noted the program was only able to detect the leanings of Twitter users, which are not reflective of the population at large. The group's results suggest that if larger data streams can be tapped in future versions, today's pollsters could find themselves out of a job.

"We feel like if you collect enough data from the population, if you get an unbiased sample from the population using this data-driven approach, the results might be more accurate," he added.

The scientists used artificial intelligence and machine learning to help the program decipher complexities in human language. The group then compared their results with the New York Times' forecasts and found the state-by-state analysis was very similar.


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