  Despite the increasing popularity of NLP in the humanities and social
sciences, advances in model performance and complexity have been accompanied by
concerns about interpretability and explanatory power for sociocultural
analysis. One popular model that balances complexity and legibility is Word
Mover's Distance (WMD). Ostensibly adapted for its interpretability, WMD has
nonetheless been used and further developed in ways which frequently discard
its most interpretable aspect: namely, the word-level distances required for
translating a set of words into another set of words. To address this apparent
gap, we introduce WMDecompose: a model and Python library that 1) decomposes
document-level distances into their constituent word-level distances, and 2)
subsequently clusters words to induce thematic elements, such that useful
lexical information is retained and summarized for analysis. To illustrate its
potential in a social scientific context, we apply it to a longitudinal social
media corpus to explore the interrelationship between conspiracy theories and
conservative American discourses. Finally, because of the full WMD model's high
time-complexity, we additionally suggest a method of sampling document pairs
from large datasets in a reproducible way, with tight bounds that prevent
extrapolation of unreliable results due to poor sampling practices.
