Sijia Yang is an assistant professor at the School of Journalism and Mass Communication at the University of Wisconsin-Madison. His research applies computational methods (e.g., text mining, web-based dynamic experiments, recommendation systems) to the study of message effects and persuasion on digital media.
Currently, he is pursuing three lines of research. The first line of work aims to understand 1) the use of moral appeals in the public information environment regarding controversial medical and scientific innovations (e.g., genomic editing, AI, emerging tobacco products); and 2) how moral appeals, especially when combined with social influences, could affect individuals’ (mis)perceptions of such innovations, behavioral outcomes, and communicative actions (e.g., clicking, retransmission, commenting). Methodologically, this line of work will explore the potential to integrate large-scale computational content analysis with web-based experiments that involve dynamic social interactions. Since digital communication campaigns increasingly utilize visual materials, the second line of work aims to evaluate the potential of applying machine learning to identify persuasive features in visual messages (e.g., pictorial social media posts from the FDA’s anti-tobaccoThe Real Costcampaign). The last line of work aims to understand the roles of algorithms in persuasive messaging, especially the potential to harness the power of algorithms (e.g., recommendation systems, persuasive chatbots) to improve the effectiveness of health communication interventions.
Sijia received his Ph.D. in communication from the Annenberg School for Communication at the University of Pennsylvania in 2019, his M.A. in communication from the University of Illinois at Urbana-Champaign in 2012, and his B.A. in English language and literature from the Renmin University of China in 2010.