The structure of online social networks modulates the rate of lexical change
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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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 13 of its 52 references that are in the map. First posted on arXiv 11 Apr 2021.
Builds on
13 of 52 references.
References · 13
- 2019Time-to-Event Prediction with Neural Networks and Cox Regressioncited by 173
- 2020ConvoKit: A Toolkit for the Analysis of Conversationscited by 71
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- 2017Community Identity and User Engagement in a Multi-Community Landscapecited by 18
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1 more
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Citing papers per year: 2022: 1; 2023: 2; 2024: 2; 2025: 0; 2026: 3.
8 papers.
- 2023Can Large Language Models Transform Computational Social Science?cited by 245
- 2024NEO-BENCH: Evaluating Robustness of Large Language Models with Neologismscited by 19
- 2024Silent Signals, Loud Impact: LLMs for Word-Sense Disambiguation of Coded Dog Whistlescited by 7
- 2023Social Meme-ing: Measuring Linguistic Variation in Memescited by 5
- 2022Networks and Identity Drive Geographic Properties of the Diffusion of Linguistic Innovationcited by 1
- 2026New Terms, New Toxicity: Consensus-based Chinese Neologism Toxicity Detection via Search-Augmented LLMscited by 0
- 2026KoNeoBench: A Curated Evaluation Dataset for LLM Understanding of Korean Neologismscited by 0
- 2026From Utterances to Networks: Modelling Slang Adoption and Diffusion Across Subredditscited by 0
- 2026From Utterances to Networks: Modelling Slang Adoption and Diffusion Across Subreddits22 Sep 2026
- 2026KoNeoBench: A Curated Evaluation Dataset for LLM Understanding of Korean Neologisms17 Sep 2026
- 2026New Terms, New Toxicity: Consensus-based Chinese Neologism Toxicity Detection via Search-Augmented LLMs5 Jul 2026
- 2024Silent Signals, Loud Impact: LLMs for Word-Sense Disambiguation of Coded Dog Whistles10 Jun 2024
- 2024NEO-BENCH: Evaluating Robustness of Large Language Models with Neologisms19 Feb 2024
- 2023Social Meme-ing: Measuring Linguistic Variation in Memes15 Nov 2023
- 2023Can Large Language Models Transform Computational Social Science?12 Apr 2023
- 2022Networks and Identity Drive Geographic Properties of the Diffusion of Linguistic Innovation10 Feb 2022