Penn’s Computational Social Science Lab’s Media Bias Detector team, under founder and director Duncan Watts, explores how people behave, how media works, how society functions, and how the human mind operates.
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Duncan Watts is charting new territory in the social sciences.
Duncan Watts (far left) and managing director Jeanne Ruane (second from left) with members of Penn’s Computational Social Science Lab.
(Image: Courtesy of Inspiring Impact)
“Superficially, computational social science takes methods from computer science and applies them to social issues. But at a deeper level, computational social science can also mean advancing our understanding of the world by solving practical problems,” says Watts. “I started this lab to embody what computational social science can be.”
Together with managing director Jeanne Ruane, Watts and his team of 23 students and researchers are exploring how people behave, how media works, how society functions, and how the human mind operates. For Watts, the key to understanding lies in our modern moment: as new technologies and data sources emerge, their applications hold new possibilities. And there are few better examples of this symbiosis than CSSLab’s Media Bias Detector.
“We’ve all experienced the divisiveness pervading popular media,” he says. “Our hypothesis is that this is exacerbated not by lies or ‘fake news’ but by bias. It’s a timely question, and in our particular historical moment, CSSLab has these incredible tools—massive new datasets and large language models, in particular—to find an answer. So we threw our energy into investigating.”
A project of the Lab’s Media Accountability Project (PennMAP), the resulting Media Bias Detector uses large language models (LLMs) to identify media bias in headline news. Unlike similar tools, the Media Bias Detector doesn’t rely on the reputation of publishers but rather on the construction and language of the articles themselves. “We can analyze stories in real time, and our tool is better able to capture the heterogeneity within different news organizations over time,” says Watts. “For example, over President Trump’s first 100 days in office, the Media Bias Detector was able to show what the media covered most heavily and also what was ignored—tariffs and protests, for example—by certain publishers.”
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