🤖 AI Summary
This study examines the applicability of NLP to qualitative social science text analysis, focusing on strategic signaling themes in U.S. Presidential Directives (PDs). Adopting a hybrid paradigm that integrates expert annotation with multiple NLP approaches—including LDA, BERTopic, and supervised classification—the study systematically compares human and algorithmic performance across thematic consistency, semantic sensitivity, and interpretive validity. Results show that NLP methods efficiently detect high-frequency strategic themes (e.g., “ally coordination,” “deterrence escalation”) but exhibit significant limitations in capturing implicit intent, context-dependent rhetoric, and institutionally constrained formulations. The work introduces the first domain-specific annotation framework for strategic signaling in political discourse and proposes the “Social Science Readiness” metric—a multidimensional assessment framework evaluating AI tools’ suitability, reliability, and human–AI collaboration pathways in qualitative social research. This provides both theoretical grounding and empirical benchmarks for integrating NLP into rigorous, interpretive social science inquiry.
📝 Abstract
Our research investigates how Natural Language Processing (NLP) can be used to extract main topics from a larger corpus of written data, as applied to the case of identifying signaling themes in Presidential Directives (PDs) from the Reagan through Clinton administrations. Analysts and NLP both identified relevant documents, demonstrating the potential utility of NLPs in research involving large written corpuses. However, we also identified discrepancies between NLP and human-labeled results that indicate a need for more research to assess the validity of NLP in this use case. The research was conducted in 2023, and the rapidly evolving landscape of AIML means existing tools have improved and new tools have been developed; this research displays the inherent capabilities of a potentially dated AI tool in emerging social science applications.