# Networks and Identity Drive Geographic Properties of the Diffusion of Linguistic Innovation

arXiv: https://arxiv.org/abs/2202.04842 · HTML: https://mlsanity.com/paper/2202.04842/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 132 references that are in the map.
- First posted on arXiv 10 Feb 2022.

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

- [Demographic Dialectal Variation in Social Media: A Case Study of African-American English](https://mlsanity.com/paper/1608.08868/) (2016)
- [Where New Words Are Born: Distributional Semantic Analysis of Neologisms and Their Semantic Neighborhoods](https://mlsanity.com/paper/2001.07740/) (2020)
- [Making "fetch" happen: The influence of social and linguistic context on nonstandard word growth and decline](https://mlsanity.com/paper/1802.04140/) (2018)
- [Geotagging One Hundred Million Twitter Accounts with Total Variation Minimization](https://mlsanity.com/paper/1404.7152/) (2014)
- [The structure of online social networks modulates the rate of lexical change](https://mlsanity.com/paper/2104.05010/) (2021)
- [Race, Religion and the City: Twitter Word Frequency Patterns Reveal Dominant Demographic Dimensions in the United States](https://mlsanity.com/paper/1605.02951/) (2016)
- [S'i o no, qu`e penses? Catalonian Independence and Linguistic Identity on Social Media](https://mlsanity.com/paper/1804.05088/) (2018)
- [Location, Occupation, and Semantics based Socioeconomic Status Inference on Twitter](https://mlsanity.com/paper/1901.05389/) (2019)
- [The role of geography in the complex diffusion of innovations](https://mlsanity.com/paper/1804.01349/) (2018)
- [Out of vocabulary words decrease, running texts prevail and hashtags coalesce: Twitter as an evolving sociolinguistic system](https://mlsanity.com/paper/1509.05096/) (2015)
- [Fading of collective attention shapes the evolution of linguistic variants](https://mlsanity.com/paper/1811.08465/) (2018)

## Built on by (most cited first, 1 of 1)

- [Evolving linguistic divergence on polarizing social media](https://mlsanity.com/paper/2309.01659/) (2023)

## Abstract (arXiv)

Adoption of cultural innovation (e.g., music, beliefs, language) is often geographically correlated, with adopters largely residing within the boundaries of relatively few well-studied, socially significant areas. These cultural regions are often hypothesized to be the result of either (i) identity performance driving the adoption of cultural innovation, or (ii) homophily in the networks underlying diffusion. In this study, we show that demographic identity and network topology are both required to model the diffusion of innovation, as they play complementary roles in producing its spatial properties. We develop an agent-based model of cultural adoption, and validate geographic patterns of transmission in our model against a novel dataset of innovative words that we identify from a 10% sample of Twitter. Using our model, we are able to directly compare a combined network + identity model of diffusion to simulated network-only and identity-only counterfactuals -- allowing us to test the separate and combined roles of network and identity. While social scientists often treat either network or identity as the core social structure in modeling culture change, we show that key geographic properties of diffusion actually depend on both factors as each one influences different mechanisms of diffusion. Specifically, the network principally drives spread among urban counties via weak-tie diffusion, while identity plays a disproportionate role in transmission among rural counties via strong-tie diffusion. Diffusion between urban and rural areas, a key component in innovation diffusing nationally, requires both network and identity. Our work suggests that models must integrate both factors in order to understand and reproduce the adoption of innovation.