In an era of rapidly evolving misinformation and disinformation, AI plays a dual role—both a tool for analyzing harmful narratives and a challenge for content moderation. This talk presents our research on how AI helps in understanding political discourse and detecting disinformation, offering key insights into social media dynamics. By examining interactions of over 6,500 U.S. state legislators on Twitter and Facebook, we demonstrate how platform policies and affordances shape political engagement in distinct ways. These differences not only reinforce partisan divides but also raise critical questions about platform-specific incentives. Beyond analysis, we develop AI-driven methods to detect conspiratorial narratives and evaluate generative AI's effectiveness in fact-checking misinformation. Comparing AI-generated credibility assessments with human annotations, our research highlights both the promise and risks of automated verification. Additionally, through collaborations with NGOs, we have gained valuable experience in deploying AI-assisted tools to support election integrity and enhance fact-checking efforts. With AI-generated disinformation on the rise, and industry shifts, including Meta's changes to fact-checking programs and X/Twitter's legal battles with watchdogs, the need to understand AI's evolving role in the information ecosystem is more critical than ever.

Yu-Ru Lin is an Associate Professor in the School of Computing and Information and the Research Director of the Institute for Cyber Law, Policy, and Security (Pitt Cyber) at the University of Pittsburgh, where she directs the PITT Computational Social Dynamics Lab (PICSO LAB). Her research lies at the intersection of Computational Social Science, Data Mining, and Visualization. She specializes in using social network and text data along with statistical learning tools and social theories to study phenomena spanning societal events and policy, anomalous behaviors, and other crucially important complex patterns concerning collective attention and actions, as well as human and social dynamics in response to societal risks. Her work has appeared in prestigious scientific venues and has been featured in the press, including WSJ, The Boston Globe, The Atlantic, MIT News, and NPR. She has authored or co-authored more than 100 refereed journal and conference papers and served on more than 50 conference program committees in the areas of big data, network science, and computational social science. She has served as a chair/co-chair of leading computational social science, web mining, and social media conferences such as AAAI ICWSM and TheWebConference/WWW (Web & Society Research Track). She currently serves as an Editor-in-Chief of AAAI ICWSM and an Associate Editor for multiple journals, including PLOS ONE, Springer EPJ Data Science, Nature's Scientific Reports, and Frontiers in Big Data. She was selected as a Fellow of Kavli Frontiers of Science, National Academy of Sciences (NAS), and was named to SAGE journal's list of ``39 Women Doing Amazing Research in Computational Social Science'' in 2019. She has been recognized as the AI 2000 "Most Influential Scholar Honorable Mention in Visualization" for her outstanding contributions to the field over the last decade (2014--2023 and 2009--2019).