Sijia Yang

Sijia Yang has been interested in applying computational methods to the study of message effects, social influences, and health communication. In past research projects, he has compared and tested recommendation algorithms to select effective anti-smoking PSAs for adult smokers, applied Structural Topic Modeling to identify discussion topics predictive of women's belief and attitude changes towards birth control methods in an online group-based health intervention, and pilot-tested a crowd-sourced approach to extracting persuasive visual features of graphic tobacco warning labels. 

Based on a pilot experiment where visual portrayals of vaping in electronic cigarette advertisements were found to unexpectedly increase harm perceptions and support for vape-free policies——especially for those morally committed to caring for others and harm prevention, Yang's dissertation seeks to understand the relationship between moral appeals and message persuasion. In particular, as misinformation continues to threaten the online information environment, his dissertation also aims to experimentally test moral appeals as a message intervention to motivate social debunking of misinformation even when the local discussion is dominated by a pro-misinformation norm.