Assistant Professor Nick Beauchamp, who studies political speech and persuasion as well as how political opinions are formed and change over time, has developed a computational tool that brings science to the challenging art of crafting persuasive text.
As the presidential race continues to heat up—the latest being that Republican presidential candidates were left fuming over what they called an unfair debate last week—the public will no doubt be more and more inundated with political messages from debates, political ads, and the campaign trail. Candidates face the challenge of cutting through the clutter and having their messages resonate with voters.
“You might think it’s like Mad Men, with people sitting on a couch and ideas somehow bubbling up, and then they share them with us.”
It’s a tricky task, for sure—but one that Northeastern assistant professor of political science Nick Beauchamp sees a new approach to address. He has created a new algorithm aimed at helping master the art of persuasive language for things like political talking points and advertisements.
“We are consuming hundreds of these kinds of messages a day, and their origins are a bit mysterious,” said Beauchamp, who is a core faculty member of the NULab for Texts, Maps, and Networks, Northeastern’s center for digital humanities and computational social science. “You might think it’s like Mad Men, with people sitting on a couch and ideas somehow bubbling up, and then they share them with us.”
Beauchamp, who studies political speech and persuasion as well as how political opinions are formed and change over time, has developed a computational tool that brings science to the challenging art of crafting persuasive text. In fact, when he tested this algorithm, he found it had a substantial impact in terms of generating persuasive text that shifted people’s opinions of President Barack Obama’s healthcare law.
