# The structure of online social networks modulates the rate of lexical change

arXiv: https://arxiv.org/abs/2104.05010 · HTML: https://mlsanity.com/paper/2104.05010/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 52 references that are in the map.
- First posted on arXiv 11 Apr 2021.

## Builds on (most cited first, 13 of 13)

- [Time-to-Event Prediction with Neural Networks and Cox Regression](https://mlsanity.com/paper/1907.00825/) (2019)
- [ConvoKit: A Toolkit for the Analysis of Conversations](https://mlsanity.com/paper/2005.04246/) (2020)
- [Continuous and Discrete-Time Survival Prediction with Neural Networks](https://mlsanity.com/paper/1910.06724/) (2019)
- [Community Identity and User Engagement in a Multi-Community Landscape](https://mlsanity.com/paper/1705.09665/) (2017)
- [Making "fetch" happen: The influence of social and linguistic context on nonstandard word growth and decline](https://mlsanity.com/paper/1802.04140/) (2018)
- [Loyalty in Online Communities](https://mlsanity.com/paper/1703.03386/) (2017)
- [Evolutionary forces in language change](https://mlsanity.com/paper/1608.00938/) (2016)
- [The Social Dynamics of Language Change in Online Networks](https://mlsanity.com/paper/1609.02075/) (2016)
- [The Road to Success: Assessing the Fate of Linguistic Innovations in Online Communities](https://mlsanity.com/paper/1806.05838/) (2018)
- [Trajectories of Blocked Community Members: Redemption, Recidivism and Departure](https://mlsanity.com/paper/1902.08628/) (2019)
- [Are All Successful Communities Alike? Characterizing and Predicting the Success of Online Communities](https://mlsanity.com/paper/1903.07724/) (2019)
- [All Who Wander: On the Prevalence and Characteristics of Multi-community Engagement](https://mlsanity.com/paper/1503.01180/) (2015)
- [Deep Dive into Anonymity: A Large Scale Analysis of Quora Questions](https://mlsanity.com/paper/1811.07223/) (2018)

## Built on by (most cited first, 8 of 8)

- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [NEO-BENCH: Evaluating Robustness of Large Language Models with Neologisms](https://mlsanity.com/paper/2402.12261/) (2024)
- [Silent Signals, Loud Impact: LLMs for Word-Sense Disambiguation of Coded Dog Whistles](https://mlsanity.com/paper/2406.06840/) (2024)
- [Social Meme-ing: Measuring Linguistic Variation in Memes](https://mlsanity.com/paper/2311.09130/) (2023)
- [Networks and Identity Drive Geographic Properties of the Diffusion of Linguistic Innovation](https://mlsanity.com/paper/2202.04842/) (2022)
- [New Terms, New Toxicity: Consensus-based Chinese Neologism Toxicity Detection via Search-Augmented LLMs](https://mlsanity.com/paper/2608.12361/) (2026)
- [KoNeoBench: A Curated Evaluation Dataset for LLM Understanding of Korean Neologisms](https://mlsanity.com/paper/2609.19916/) (2026)
- [From Utterances to Networks: Modelling Slang Adoption and Diffusion Across Subreddits](https://mlsanity.com/paper/2609.25669/) (2026)

## Abstract (arXiv)

New words are regularly introduced to communities, yet not all of these words persist in a community's lexicon. Among the many factors contributing to lexical change, we focus on the understudied effect of social networks. We conduct a large-scale analysis of over 80k neologisms in 4420 online communities across a decade. Using Poisson regression and survival analysis, our study demonstrates that the community's network structure plays a significant role in lexical change. Apart from overall size, properties including dense connections, the lack of local clusters and more external contacts promote lexical innovation and retention. Unlike offline communities, these topic-based communities do not experience strong lexical levelling despite increased contact but accommodate more niche words. Our work provides support for the sociolinguistic hypothesis that lexical change is partially shaped by the structure of the underlying network but also uncovers findings specific to online communities.