  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.
