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Despite the increasing popularity of NLP in the humanities and socialsciences, advances in model performance and complexity have been accompanied byconcerns about interpretability and explanatory power for socioculturalanalysis. One popular model that balances complexity and legibility is WordMover's Distance (WMD). Ostensibly adapted for its interpretability, WMD hasnonetheless been used and further developed in ways which frequently discardits most interpretable aspect: namely, the word-level distances required fortranslating a set of words into another set of words. To address this apparentgap, we introduce WMDecompose: a model and Python library that 1) decomposesdocument-level distances into their constituent word-level distances, and 2)subsequently clusters words to induce thematic elements, such that usefullexical information is retained and summarized for analysis. To illustrate itspotential in a social scientific context, we apply it to a longitudinal socialmedia corpus to explore the interrelationship between conspiracy theories andconservative American discourses. Finally, because of the full WMD model's hightime-complexity, we additionally suggest a method of sampling document pairsfrom large datasets in a reproducible way, with tight bounds that preventextrapolation of unreliable results due to poor sampling practices.

WMDecompose: A Framework for Leveraging the Interpretable Properties of Word Mover's Distance in Sociocultural Analysis