Problem of Building Synthetic Psychological Data: Experience in Modeling Reactions to Frustration

Abstract

The issue of generating synthetic data for psychological research remains relevant and complex. Problems of the confidentiality, reliability, validity, and accuracy of conclusions are unevenly represented in different areas of psychology and are actually related to the use of synthetic data in related sciences, such as medicine, sociology, history, political science, and economics. The study of various psychological phenomena in large social groups is associated with the analysis of complex and difficult-to-formalize constructs. Synthetic data refers to artificially generated data based on algorithms and modeling. S. Rosenzweig’s classification of types of reactions to frustration was chosen as the basis for this study. When analyzing online discourse, there is a problem of the low number of certain types. This is especially true for the class of impunitive reactions. The paper analyzes the possibility of the creation of text corpora using synthetic data of reactions to frustration that is generated by large language models. In the experiments, the experts created prompts and generated examples of impulsive reactions using four large language models, with 10 examples of each type of reaction. They then evaluated the contextual validity and quality of the generated responses. The results allowed them to identify the weaknesses in generating texts with complex psychological phenomena for training neural network models.