Social situations are key determinants of cognition and behavior, and although several frameworks for representing situations have been proposed, these remain partial, nonintegrated, and not systematically mapped onto the rich space of situations encountered in everyday life. We address this problem by analyzing more than 20,000 detailed textual descriptions of dyadic social interactions obtained from participant-generated stories, published fiction, blogs, and autobiographical narratives. Our main methodological contribution is to use generative artificial intelligence to code these textual descriptions along a very large set of features and derive a detailed taxonomy of situational classes or categories of social interactions. We subsequently relate these situational classes to high-level situational variables like conflict, power, and duty, which have been identified by prior theory. In this way, our article provides a comprehensive, data-driven, and integrative framework for quantifying situational structure, advancing the study of social cognition and behavior.
Bhatia et al. (Tue,) studied this question.