Assistant Professor Brian C. Keegan is a computational social scientist whose research is at the intersection of human-computer interaction, network science and data science. He uses computational methods to identify, analyze, and theorize the structural and temporal patterns of large-scale social interaction. This work is motivated by his belief that social life rarely unfolds at a steady state; bursts, sequences, and other dynamics play crucial roles in structuring the social world around us. His research uses large datasets from socio-technical information systems such as Wikipedia revision histories, online game user behavior logs, and social media data streams to understand the intersection of temporal dynamics and large-scale social behavior. He is also researching how data science capabilities are being adopted by the emerging cannabis industry.
Computational social science, Network science, Data science, Computer-mediated communication, New media, Peer production, Crowdsourcing, Computer-supported cooperative work, Web science, Cannabis, Marijuana