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Ágnes Horvát

WED@NICO SEMINAR: Ágnes Horvát, Northwestern School of Communication "The Academic Use of Social Media, LLMs and AI-Assisted Decision-Making"

Wednesday, March 4, 2026 | 12:00 PM - 1:00 PM CT
Chambers Hall, Lower Level, 600 Foster St, Evanston, IL 60208 map it

Speaker:

Ágnes Horvát, Associate Professor, Department of Communication Studies, Northwestern School of Communication

Title:

The Academic Use of Social Media, LLMs and AI-Assisted Decision-Making

Abstract: 

In the digital era social media and large language models (LLMs) are reshaping scholarly communication with substantial implications for visibility, publishing, and hiring. In the first part of this talk, I present our research documenting a systematic gender gap in how scientists self-promote their work on social media platforms. I then introduce follow-up survey research that investigates the mechanisms underlying this disparity and experimentally tests whether informing scholars about the gap influences their future intentions to self-promote. The second part of my talk examines the growing role of LLMs in scientific writing. Drawing on an analysis of more than 15 million biomedical abstracts, we identify abrupt vocabulary shifts consistent with LLM-assisted writing, suggesting that a substantial share of recent abstracts (at least 13.5% in 2024) has been shaped by these systems. Our findings underscore the rapid integration of LLMs into scholarly practice and raise important questions about linguistic homogenization, authorship norms, and the future of scientific communication. Finally, I present ongoing experimental research on AI-assisted decision-making. Using controlled experiments that model academic hiring as hidden-profile tasks, we compare the effects of individual AI decision aids and group-level AI facilitators on decision accuracy and participants' satisfaction with the evaluation process. Taken together, these projects illuminate how digital technologies interact with human behavior to shape whose work gains visibility, how research is written and presented, and how consequential academic decisions may be improved.

Speaker Bio:

Ágnes Horvát is an Associate Professor of Communication Studies and Computer Science (by courtesy) at Northwestern University, and director of the Lab on Innovation, Networks, and Knowledge (LINK). Her research lies in human-centered computing and network science and investigates how online spaces operate and disseminate information. Her group strives to make digital tools more efficient for scientists, entrepreneurs, and creative artists. Her recent projects investigate the use of LLMs in scientific writing and music creation, study biases in online attention to science, identify cases of collective intelligence and opportunities for improved decision-making, and develop frameworks to examine persuasion and opinion change in online discussions. Her work has been awarded an NSF CAREER, CRII, and collaborative awards as PI. Her doctoral advisees have received highly competitive prizes, including a Northwestern Presidential Fellowship and best student paper awards at international conferences. Her research has been featured recently in Nature, The New York Times, The Washington Post, Le Monde, The Economist, and Frankfurter Allgemeine Zeitung.

Location:

In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/96701776160
PW: NICO26

About the Speaker Series:

Wednesdays@NICO is a vibrant weekly seminar series focusing broadly on the topics of complex systems, data science and network science. It brings together attendees ranging from graduate students to senior faculty who span all of the schools across Northwestern, from applied math to sociology to biology and every discipline in-between. Please visit: https://bit.ly/WedatNICO for information on future speakers.

Cost: Free

Audience

  • Faculty/Staff
  • Student
  • Public
  • Post Docs/Docs
  • Graduate Students

Contact

Emily Rosman   (847) 491-2527

nico@northwestern.edu

Interest

  • Academic (general)
  • Data Science & AI

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