CONTACT: William Sethares, (608) 262-5669,; Lewis Friedland,, (608) 263-7853

MADISON – Researchers at the University of Wisconsin-Madison are using computers in new ways to develop a comprehensive picture of how people communicate about politics, and how those conversations can be shaped by media, social networks and personal interactions.

What their computer analysis finds, the researchers hope, could help bridge the divide between people on either side of the political aisle who are unable to come together to solve society’s problems because they can’t even talk to each other – so much so that they might as well be speaking different languages.

“One of the most important questions for us is: Does the communication system help people to understand the problems they define in their social and political lives?” says Lewis Friedland, a professor in UW-Madison’s School of Journalism and Mass Communication. “Or, do we have a system that actually exacerbates divisions among people – that makes it easier to divide up into ‘ingroups’ and ‘outgroups,’ to see others as unlike us or unworthy?”

Drawing on social media posts, public opinion polling, news coverage and in-person interviews from across Wisconsin stretching back to 2010, Friedland and collaborators will paint a picture of political interactions as a living, changing environment – a “communication ecology” – with webs of interaction between people and institutions in the state. Supported by funding from the UW2020 initiative, it is one of the most ambitious efforts ever to understand how people in an entire state talk about politics, and how those conversations have changed over time.

“No one has attempted to model communication ecologies on a statewide level, especially over eight years,” says Friedland. “It takes enormous creativity in gathering data, modeling relationships and developing analysis methods.”

The researchers are harnessing the power of machine learning, in which UW-Madison is a leading innovator, to detect how people of opposite political persuasions assign different meanings to the same words.

For example, the word “regulation” can carry substantially different connotations – “helpful and necessary” or “onerous and invasive” – for liberals and conservatives. While those sentiments might seem intuitive, it’s difficult to rigorously define and quantify exactly how people assign meanings to words.

Machine learning offers a solution to that problem by transforming words into geometric concepts called vectors and using mathematical operations to make comparisons.


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