Jon's research focuses on the causes and consequences of, as well as solutions to, rising distrust in sources of expert information (e.g., science, health). He has a special interest in exploring persuasion at the intersection of health and politics, which includes studying phenomena such as the politicization of science and health, political polarization, filter bubbles/echo chambers, the emerging post-truth world, and information warfare. It also includes seeking heteorgeneity in the findings across particular demographics at high socioeconomic and health risk. He draws on theories and methods from his uniquely interdisciplinary set of educational, research, and professional experiences, including those from experimental and behavioral economics, political science, psychology, and machine learning. He is currently using machine learning-based text analytics to explore how trust/distrust in sources of expert information is discussed on traditional and social media -- followed by the use of online randomized controlled survey experiments to test the causal effects of particular persuasion strategies on perceptions of trust/distrust, as well as other important behavioral outcomes of interest.