Media Literacy

Lightning talks feature five-minute, rapid fire presentations with time for questions.

Moderated by Angela Lee, Stanford University

• Fact-checking Information Generated by a Large Language Model can decrease Headline Discernment
  Matthew DeVerna, Indiana University
• Thoroughly Tracking the Takes and Trajectories of News Narratives from Trustworthy and Worrisome Websites
  Hans Hanley, Stanford University
• Navigating Online Information Spaces: Strategies to Counteract Online Misinformation and Enhance Trust
  Lonnie Shumsky, Stanford Social Media Lab
• Reducing Misinformation Sharing at Scale using Digital Accuracy Prompt Ads
  Hause Lin, Massachusetts Institute of Technology
• Correcting Misinformation with a Large Language Model
  Xinyi Zhou, University of Washington
• The Effect of AI Labeling on Perceptions of Images
  Zeve Sanderson, NYU Center for Social Media & Politics
• Community-based fact-checking reduces the spread of misleading posts on social media
  Thomas Renault, Université Paris 1 Panthéon – Sorbonne
• Building Resilience to Misinformation in Communities of Color: Results from Two Studies of Tailored Digital Media Literacy Interventions
  Ryan Moore, Stanford University
• How Scientific Retractions Enable Further Misinformation (and What to Do About it)
  Rod Abhari, Northwestern University
• Labeling AI-Generated Content: Promises, Perils, and Future Directions
  Zivvy Epstein, MIT