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Tribal advocates keep up legal pressure for fair political maps; 12-member jury sworn in for Trump's historic criminal trial; Healthcare decision planning important for CT residents; Debt dilemma poll: Hoosiers wrestle with college costs.

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Civil Rights activists say a court ruling could end the right to protest in three southern states, a federal judge lets January 6th lawsuits proceed against former President Trump and police arrest dozens at a Columbia University Gaza protest.

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Rural Wyoming needs more vocational teachers to sustain its workforce pipeline, Ohio environmental advocates fear harm from a proposal to open 40-thousand forest acres to fracking and rural communities build bike trail systems to promote nature, boost the economy.

New Kind of Polling: What Can the Tweets Tell Us?

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Monday, November 7, 2016   

RICHMOND, Va. -- Americans have learned a lot about how Donald Trump feels about people through Twitter. But how do Twitter users feel about Donald Trump? Computer scientists from the University of Utah have developed what they call "sentiment analysis" software that can determine how voters are feeling based on what they write or say.

Feifei Li, an associate professor at the University who helped develop the program, 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. If you look at the last few months of data altogether, the sentiments for Democrats is stronger than the sentiments for Republicans,” Li said. "Given the recent outburst of email scandals, things might change a little bit."

Researchers said the biggest surge of positive tweets for Republicans came during the national convention, and again after a 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, who are not necessarily reflective of the population at large.

The group's results suggested that if larger data streams could 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,” Li said.

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.

More information on the study and its findings is available here.


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