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AI Seminar

AI Seminar: David Jurgens – Does chocolate really cure cancer? Modeling Information Change in Science Communication

David JurgensAssistant Professor, School of InformationUniversity of Michigan
3725 Beyster BuildingMap


https://umich.zoom.us/j/92216884113 (password: UMichAI)

Title: Does chocolate really cure cancer? Modeling Information Change in Science Communication


Public trust in science is dependent, in part, on journalists accurately reporting the latest scientific advances. The process of reporting on science requires careful effort to faithfully translate academic jargon into descriptions more accessible to the general public. Yet, occasionally this process mimics the telephone game where scientific results are changed with each reporting until they no longer resemble what was actually learned—e.g., chocolate now cures cancer. Here, we ask to what degree is science journalism accurately conveying science? To answer this question, I will describe two works in an ongoing effort from my group to quantify the communication quality of scientific journalism. In the first part, I focus on how certain journalists describe scientific findings compared with the scholars. Commonly, certainty is measured with heuristics like hedges (e.g., “possibly”). However, certainty is a complex construct, with authors expressing not only the degree but the type and aspects of uncertainty in order to give the reader a specific impression of what is known. I will show that hedges alone account for only a partial explanation of certainty and, by building new computational models, show how both scholars and journalists vary in how and when findings are described with certainty. In the second part, I will focus on the more general task of measuring whether the information of a finding changes in journalistic descriptions. I will introduce a new resource and model for aligning scientific findings from news stories, social media discussions, and full texts of academic papers. I will show how this new resource can improve downstream performance on evidence retrieval for fact checking of real-world scientific claims and, through applying the model to millions of science-news reports, can reveal large-scale trends in the degrees to which people and organizations faithfully communicate new scientific findings.


David Jurgens is an assistant professor in the School of Information at the University of Michigan. He holds a PhD from the University of California Los Angeles and was a postdoctoral scholar in the Department of Computer Science at Stanford University and prior at McGill University. His research combines natural language processing, network science and data science to discover, explain and predict human behavior in large social systems. His research has been published in top computational social science and natural language processing venues including PNAS, WWW, ACL, ICWSM, EMNLP, and others. His work has won the Cozzarelli Prize from the National Academy of Science, Cialdini Prize from the Society for Personality and Social Psychology, best paper at ICWSM and W-NUT, best paper nomination at ACL and Web Science, and has been featured in news outlets such as the BBC, Time, MIT Technology Review, New Scientist, and Forbes.

This AI Seminar was sponsored by LG AI Research.


AI Lab

Faculty Host

Lu Wang