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.

翻译：用于预测用户行为的监督式机器学习模型提出了一个具有挑战性的分类问题，其平均预测性能得分低于其他文本分类任务。本研究评估了基于认知评价理论的多任务学习框架，以用户自我表达和心理属性为函数来预测用户行为。我们的实验表明，结合用户语言和特质进行预测，其效果优于仅基于文本的预测模型。研究结果凸显了将心理学构念融入自然语言处理（NLP）以增强对用户行为的理解与预测的重要性。最后，我们探讨了大型语言模型在计算心理学领域未来应用的启示。