Computational Text Analysis for Building and Testing Social Theory

Digitization and advances in natural language processing have transformed how sociologists can measure, model, and interpret social life through text. We provide an overview of computational text analysis as a methodological tool kit for building and testing social theory. The field is moving from descriptive uses toward theory-driven and causal inference approaches, though methodological standards—especially around data quality, reproducibility, and causal claims—remain inconsistent. Organizing approaches into data-first, theory-first, and theory–data integration paradigms, we highlight how different methods each balance inductive discovery with theoretical specification. We conceptualize text-analytic methods as measurement strategies that extract sociologically relevant information from unstructured language data and show how they can be incorporated into both thick descriptions and causal inference workflows. Taken together, various computational text analysis approaches offer researchers new opportunities to recover latent constructs, bridge quantitative scale with qualitative depth, and revitalize interpretive approaches in sociology.