# The Geometry of Culture: Analyzing Meaning through Word Embeddings

arXiv: https://arxiv.org/abs/1803.09288 · HTML: https://mlsanity.com/paper/1803.09288/

## Key facts

- Cited by 87 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2018.

## Built on by (most cited first, 30 of 87)

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- [Identification of Biased Terms in News Articles by Comparison of Outlet-specific Word Embeddings](https://mlsanity.com/paper/2112.07384/) (2021)
- [WordBias: An Interactive Visual Tool for Discovering Intersectional Biases Encoded in Word Embeddings](https://mlsanity.com/paper/2103.03598/) (2021)
- [What company do words keep? Revisiting the distributional semantics of J.R. Firth & Zellig Harris](https://mlsanity.com/paper/2205.07750/) (2022)
- [Representation Of Lexical Stylistic Features In Language Models' Embedding Space](https://mlsanity.com/paper/2305.18657/) (2023)
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- [Semantic Structure in Large Language Model Embeddings](https://mlsanity.com/paper/2508.10003/) (2025)
- [Introduction to Neural Transfer Learning with Transformers for Social Science Text Analysis](https://mlsanity.com/paper/2102.02111/) (2021)
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- [Theoretical foundations and limits of word embeddings: what types of meaning can they capture?](https://mlsanity.com/paper/2107.10413/) (2021)
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- [Concept-Guided Chain-of-Thought Prompting for Pairwise Comparison Scoring of Texts with Large Language Models](https://mlsanity.com/paper/2310.12049/) (2023)
- [On the Interpretability and Significance of Bias Metrics in Texts: a PMI-based Approach](https://mlsanity.com/paper/2104.06474/) (2021)
- [Using Sociolinguistic Variables to Reveal Changing Attitudes Towards Sexuality and Gender](https://mlsanity.com/paper/2109.11061/) (2021)
- [Measuring Intersectional Biases in Historical Documents](https://mlsanity.com/paper/2305.12376/) (2023)
- [Adjusting Interpretable Dimensions in Embedding Space with Human Judgments](https://mlsanity.com/paper/2404.02619/) (2024)
- [Navigating the Risks of Using Large Language Models for Text Annotation in Social Science Research](https://mlsanity.com/paper/2503.22040/) (2025)
- [Word Embedding for Social Sciences: An Interdisciplinary Survey](https://mlsanity.com/paper/2207.03086/) (2022)
- [Do Large GPT Models Discover Moral Dimensions in Language Representations? A Topological Study Of Sentence Embeddings](https://mlsanity.com/paper/2309.09397/) (2023)

## Abstract (arXiv)

We demonstrate the utility of a new methodological tool, neural-network word embedding models, for large-scale text analysis, revealing how these models produce richer insights into cultural associations and categories than possible with prior methods. Word embeddings represent semantic relations between words as geometric relationships between vectors in a high-dimensional space, operationalizing a relational model of meaning consistent with contemporary theories of identity and culture. We show that dimensions induced by word differences (e.g. man - woman, rich - poor, black - white, liberal - conservative) in these vector spaces closely correspond to dimensions of cultural meaning, and the projection of words onto these dimensions reflects widely shared cultural connotations when compared to surveyed responses and labeled historical data. We pilot a method for testing the stability of these associations, then demonstrate applications of word embeddings for macro-cultural investigation with a longitudinal analysis of the coevolution of gender and class associations in the United States over the 20th century and a comparative analysis of historic distinctions between markers of gender and class in the U.S. and Britain. We argue that the success of these high-dimensional models motivates a move towards "high-dimensional theorizing" of meanings, identities and cultural processes.