Computational Approaches and Message Effects Research (CAMER)

The Computational Approaches and Message Effects Research (CAMER) group, directed by Sijia Yang, studies persuasive messages as they are produced, promulgated, and processed in the broader digital informational environment. Our group is particularly interested in exploring how computational methods (e.g., text mining, computer vision, algorithms, geospatial modeling) can help advance our understanding of how persuasive messaging works as it intersects with psychological processes, social dynamics, and technological affordances. Currently, we are focusing on the following lines of research: 1) visual persuasion and the feasibility of employing computer vision techniques to predict and explain the persuasiveness and share-worthiness of pictorial substance control messages (e.g., emerging tobacco products, recreational use of cannabis); 2) the temporal and geospatial dynamics of moral appeals and their persuasive impacts around emerging and oftentimes controvertial health issues (e.g., the COVID-19 pandemic, vaccination, CRISPR); 3) misinformation classification and correction, especially the joint impacts of messages and social dynamics on peer fact-checking; 4) just-in-time message tailoring in mhealth interventions (e.g., opioid addiction treatment, COVID-19 mitigation); and 5) the persuasiveness of positive appeals (e.g., hope, transcendence). Faculty Leader: Sijia Yang

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