2024/07/11 by Gerard Christopher Yeo, Yeo, Gerard Christopher, Shaz Furniturewala +3
Psychology · #Computation and Language (cs.CL) #Emotions and Moral Behavior #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2407.08182
openalex publication_date 2024/07/11 · openalex created_date 2024/07/14 · openalex updated_date 2026/07/28
Supervised machine-learning models for predicting user behavior offer a challenging classification problem with lower average prediction performance scores than other text classification tasks. This study evaluates multi-task learning frameworks grounded in Cognitive Appraisal Theory to predict user behavior as a function of users' self-expression and psychological attributes. Our experiments show that users' language and traits improve predictions above and beyond models predicting only from text. Our findings highlight the importance of integrating psychological constructs into NLP to enhance the understanding and prediction of user actions. We close with a discussion of the implications for future applications of large language models for computational psychology.