# The Social Dynamics of Language Change in Online Networks

arXiv: https://arxiv.org/abs/1609.02075 · HTML: https://mlsanity.com/paper/1609.02075/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2016.

## Built on by (most cited first, 9 of 9)

- [The structure of online social networks modulates the rate of lexical change](https://mlsanity.com/paper/2104.05010/) (2021)
- [Do Not Fire the Linguist: Grammatical Profiles Help Language Models Detect Semantic Change](https://mlsanity.com/paper/2204.05717/) (2022)
- [Abolitionist Networks: Modeling Language Change in Nineteenth-Century Activist Newspapers](https://mlsanity.com/paper/2103.07538/) (2021)
- [Predicting Long-Term Citations from Short-Term Linguistic Influence](https://mlsanity.com/paper/2210.13628/) (2022)
- [A Survey on Time-Series Distance Measures](https://mlsanity.com/paper/2412.20574/) (2024)
- [The Role of Network and Identity in the Diffusion of Hashtags](https://mlsanity.com/paper/2407.12771/) (2024)
- [No Time Like the Present: Effects of Language Change on Automated Comment Moderation](https://mlsanity.com/paper/2207.04003/) (2022)
- [Boosting classification reliability of NLP transformer models in the long run](https://mlsanity.com/paper/2302.10016/) (2023)
- [Algorithmic Cultivation: How Social Media Feeds Shape User Language](https://mlsanity.com/paper/2605.17010/) (2026)

## Abstract (arXiv)

Language change is a complex social phenomenon, revealing pathways of communication and sociocultural influence. But, while language change has long been a topic of study in sociolinguistics, traditional linguistic research methods rely on circumstantial evidence, estimating the direction of change from differences between older and younger speakers. In this paper, we use a data set of several million Twitter users to track language changes in progress. First, we show that language change can be viewed as a form of social influence: we observe complex contagion for phonetic spellings and "netspeak" abbreviations (e.g., lol), but not for older dialect markers from spoken language. Next, we test whether specific types of social network connections are more influential than others, using a parametric Hawkes process model. We find that tie strength plays an important role: densely embedded social ties are significantly better conduits of linguistic influence. Geographic locality appears to play a more limited role: we find relatively little evidence to support the hypothesis that individuals are more influenced by geographically local social ties, even in their usage of geographical dialect markers.