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

arXiv: https://arxiv.org/abs/2110.07330 · HTML: https://mlsanity.com/paper/2110.07330/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 57 references that are in the map.
- First posted on arXiv 14 Oct 2021.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [A Survey of the State of Explainable AI for Natural Language Processing](https://mlsanity.com/paper/2010.00711/) (2020)
- [Word Rotator's Distance](https://mlsanity.com/paper/2004.15003/) (2020)
- [Modeling Interpersonal Linguistic Coordination in Conversations using Word Mover's Distance](https://mlsanity.com/paper/1904.06002/) (2019)
- [Roots of Trumpism: Homophily and Social Feedback in Donald Trump Support on Reddit](https://mlsanity.com/paper/2005.01790/) (2020)
- [What Makes People Join Conspiracy Communities?: Role of Social Factors in Conspiracy Engagement](https://mlsanity.com/paper/2009.04527/) (2020)
- [Speeding up Word Mover's Distance and its variants via properties of distances between embeddings](https://mlsanity.com/paper/1912.00509/) (2019)
- [Online Political Discourse in the Trump Era](https://mlsanity.com/paper/1711.05303/) (2017)
- [Linear-Complexity Relaxed Word Mover's Distance with GPU Acceleration](https://mlsanity.com/paper/1711.07227/) (2017)
- [An Efficient Shared-memory Parallel Sinkhorn-Knopp Algorithm to Compute the Word Mover's Distance](https://mlsanity.com/paper/2005.06727/) (2020)
- [Topic Modeling on User Stories using Word Mover's Distance](https://mlsanity.com/paper/2007.05302/) (2020)

## Abstract (arXiv)

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.