# Human Languages with Greater Information Density Increase Communication Speed, but Decrease Conversation Breadth

arXiv: https://arxiv.org/abs/2112.08491 · HTML: https://mlsanity.com/paper/2112.08491/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 114 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 11 of 11)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016)
- [Linear Algebraic Structure of Word Senses, with Applications to Polysemy](https://mlsanity.com/paper/1601.03764/) (2016)
- [The Geometry of Culture: Analyzing Meaning through Word Embeddings](https://mlsanity.com/paper/1803.09288/) (2018)
- [On the universal structure of human lexical semantics](https://mlsanity.com/paper/1504.07843/) (2015)
- [Integrating topic modeling and word embedding to characterize violent deaths](https://mlsanity.com/paper/2106.14365/) (2021)

## Built on by (most cited first, 5 of 5)

- [Statistical Uncertainty in Word Embeddings: GloVe-V](https://mlsanity.com/paper/2406.12165/) (2024)
- [Word Embedding for Social Sciences: An Interdisciplinary Survey](https://mlsanity.com/paper/2207.03086/) (2022)
- [Balancing Complexity and Informativeness in LLM-Based Clustering: Finding the Goldilocks Zone](https://mlsanity.com/paper/2504.04314/) (2025)
- [Tripartite-GraphRAG via Plugin Ontologies](https://mlsanity.com/paper/2504.19667/) (2025)
- [Characterizing Human Actions in the Digital Platform by Temporal Context](https://mlsanity.com/paper/2206.09535/) (2022)

## Abstract (arXiv)

Human languages vary widely in how they encode information within circumscribed semantic domains (e.g., time, space, color, human body parts and activities), but little is known about the global structure of semantic information and nothing about its relation to human communication. We first show that across a sample of ~1,000 languages, there is broad variation in how densely languages encode information into their words. Second, we show that this language information density is associated with a denser configuration of semantic information. Finally, we trace the relationship between language information density and patterns of communication, showing that informationally denser languages tend toward (1) faster communication, but (2) conceptually narrower conversations within which topics of conversation are discussed at greater depth. These results highlight an important source of variation across the human communicative channel, revealing that the structure of language shapes the nature and texture of human engagement, with consequences for human behavior across levels of society.