John Bollenbacher

John Bollenbacher is a Research Data Scientist with a background in computational social science and physics. Dr. Bollenbacher applies state-of-the-art methods in causal inference, natural language processing, and artificial intelligence to help domain experts conduct quantitative research and accelerate their qualitative research through AI automation. His work spans multiple domains including public health, environment science, and computational social science.

His recent portfolio of work focuses on three methodological themes: 1. Analyzing social media data to understand public health trends and measure public health impacts of social media, 2. Using generative AI to help automate repetitive workflows in qualitative research, and 3. Building quantitative models of social and environmental phenomena to estimate their impacts. 
 
Dr. Bollenbacher's work on social media data includes analyzing the impacts of antivaccine twitter content on public health outcomes during COVID-19 and analyzing social media activity related to tobacco products and public health campaigns. In the area of generative AI, he has played a significant role in deploying generative AI in over 10 projects across RTI, including automating deductive coding of texts, summarizing research articles for literature reviews, and helping writers generate plain language versions of technical texts.  Finally, his recent quantitative modeling work has recently included spatiotemporal models of crime occurrences and their social determinates, models to help estimate carbon sequestration in forests under changing climate conditions, and models of public health outcomes under no-smoking policies in multifamily housing. 
 
Before joining RTI, John worked with Indiana University’s Observatory on Social Media to study the impacts of social media on public health and political discourse.