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America's 'Radical Elders' continue their work for fairness, justice; SCOTUS upholds law disarming domestic abusers; Workplace adoption benefits help families, communities; Report examines barriers to successful post-prison re-entry in NC.

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A congresswoman celebrates Biden protections for mixed status families, Louisiana's Ten Commandments law faces an inevitable legal challenge, and a senator moves to repeal the strict 19th century anti-obscenity and anti-abortion Comstock Act.

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A Minnesota town claims the oldest rural Pride Festival while rural educators say they need support to teach kids social issues, rural businesses can suffer when dollar stores come to town and prairie states like South Dakota are getting help to protect grasslands.

Want a Window into the Election? Look at Twitter

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Monday, November 7, 2016   

SEATTLE — 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, which 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 its 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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