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

Moderated by Izzy Gainsburg, Stanford Polarization and Social Change Lab

• Exploring the Interaction of Trust in Science and Vaccine Hesitancy
  Pranav Goel, Northeastern University
• Otherization via Disinformation: Text, Context and the Bahaʼis in Iran
  Fares Hedayati, Baha’i International Community
• Foreign Information Manipulation and Interference: Lessons from the EU Elections
  Rachele Gilman, Global Disinformation Index
• Understanding Online Hate Speech in Context
  Thomas Davidson, Rutgers University
• The Musk Effect: Changes in Twitter’s Misinformation and Partisan Composition
  Burak Oztura, Northeastern University
• Measuring the Effects of Harmful Social Media Narratives in Conflict Settings
  Bailey Ulbricht, Stanford Law School
• Where Do Election Deniers Get their News?
  Hong Qu, Northeastern University
• Revolutionary Rhetoric: Moderating the fine line between Patriotism and Dangerous Speech
  Cathy Buerger, Dangerous Speech Project
• Election Misinformation: A Case Study from Shasta County California
  Paul Spencer, Disability Rights California
• Hate Speech and Misinformation on WhatsApp: Insights from a Large Data Donation Program in India & Brazil
  Kiran Garimella, Rutgers University
• Content Moderation to Prevent or Counter Violent Radicalism in Pakistan: Perspectives of Social Media Activists
  Muhammad Rizwan Safdar

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

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

Moderated by Samidh Chakrabarti, Stanford University

• Using LLMs for Labeling Task: Progress and Potential Risks
  Dave Willner, Stanford University
• GenAI/LLMs tech is Swiss Army Knife for Guardians of the Internet
  Shiwani Gupta, Google
• Navigating the Landscape of Automated Content Moderation: Insights from Ofcom’s Research
  Pedro Freire, Ofcom – UK Office of Communications
• Utility of Generative AI vs Discriminative AI for Content Moderation
  Tom Siegel, TrustLab, Inc
• Identifying Best Practices for the Use of AI and Automation to Detect, Enforce, and Review Abusive Content and Behavior
  David Sullivan, Digital Trust & Safety Partnership
• Harmful YouTube Video Detection: A Taxonomy of Online Harm and MLLMs (GPT) as Alternative Annotators
  Claire Wonjeong Jo, University of California Davis
• Contested Pathways to Trusted and Safe AI through Third-Party Audits
  Chris Tenove, University of British Columbia
• Lessons Learned: Prepping for AI Automation in Trust & Safety Operations
  Jimin Lee, Change.org