# Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang

arXiv: https://arxiv.org/abs/2203.04651 · HTML: https://mlsanity.com/paper/2203.04651/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 86 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings](https://mlsanity.com/paper/1909.00512/) (2019)
- [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016)
- [SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection](https://mlsanity.com/paper/2007.11464/) (2020)
- [Analysing Lexical Semantic Change with Contextualised Word Representations](https://mlsanity.com/paper/2004.14118/) (2020)
- [Statistically Significant Detection of Linguistic Change](https://mlsanity.com/paper/1411.3315/) (2014)
- [Temporal Analysis of Language through Neural Language Models](https://mlsanity.com/paper/1405.3515/) (2014)
- [Explaining and Improving BERT Performance on Lexical Semantic Change Detection](https://mlsanity.com/paper/2103.07259/) (2021)
- [UiO-UvA at SemEval-2020 Task 1: Contextualised Embeddings for Lexical Semantic Change Detection](https://mlsanity.com/paper/2005.00050/) (2020)
- [A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains](https://mlsanity.com/paper/1906.02979/) (2019)
- [Capturing Evolution in Word Usage: Just Add More Clusters?](https://mlsanity.com/paper/2001.06629/) (2020)
- [Learning to Explain Non-Standard English Words and Phrases](https://mlsanity.com/paper/1709.09254/) (2017)
- [Frequency-based Distortions in Contextualized Word Embeddings](https://mlsanity.com/paper/2104.08465/) (2021)
- [On the Linearity of Semantic Change: Investigating Meaning Variation via Dynamic Graph Models](https://mlsanity.com/paper/1704.02497/) (2017)
- [A Computational Framework for Slang Generation](https://mlsanity.com/paper/2102.01826/) (2021)
- [Grammatical Profiling for Semantic Change Detection](https://mlsanity.com/paper/2109.10397/) (2021)
- [Simple Models for Word Formation in English Slang](https://mlsanity.com/paper/1804.02596/) (2018)
- [Polysemy and brevity versus frequency in language](https://mlsanity.com/paper/1904.00812/) (2019)
- [Frequency patterns of semantic change: Corpus-based evidence of a near-critical dynamics in language change](https://mlsanity.com/paper/1703.00203/) (2017)
- [IMS at SemEval-2020 Task 1: How low can you go? Dimensionality in Lexical Semantic Change Detection](https://mlsanity.com/paper/2008.03164/) (2020)

## Built on by (most cited first, 15 of 15)

- [On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMs](https://mlsanity.com/paper/2407.19200/) (2024)
- [A Survey on Contextualised Semantic Shift Detection](https://mlsanity.com/paper/2304.01666/) (2023)
- [A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Change](https://mlsanity.com/paper/2402.12011/) (2024)
- [Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models](https://mlsanity.com/paper/2305.12544/) (2023)
- [Toward Informal Language Processing: Knowledge of Slang in Large Language Models](https://mlsanity.com/paper/2404.02323/) (2024)
- [The Lou Dataset -- Exploring the Impact of Gender-Fair Language in German Text Classification](https://mlsanity.com/paper/2409.17929/) (2024)
- [Understanding Gen Alpha Digital Language: Evaluation of LLM Safety Systems for Content Moderation](https://mlsanity.com/paper/2505.10588/) (2025)
- [Tracing Semantic Variation in Slang](https://mlsanity.com/paper/2210.08635/) (2022)
- [SlangDIT: Benchmarking LLMs in Interpretative Slang Translation](https://mlsanity.com/paper/2505.14181/) (2025)
- [ImmunoLingo: Linguistics-based formalization of the antibody language](https://mlsanity.com/paper/2209.12635/) (2022)
- [Rethinking Word Similarity: Semantic Similarity through Classification Confusion](https://mlsanity.com/paper/2502.05704/) (2025)
- [A Study of Slang Representation Methods](https://mlsanity.com/paper/2212.05613/) (2022)
- [The BD-LSC Dataset: Facilitating the Benchmarking of Models for Lexical Semantic Change Detection in Slang and Standard Usage](https://mlsanity.com/paper/2606.16560/) (2026)
- [Dynamics of Meaning: Towards the Evaluation of Diachronic Semantic Change in Sinhala](https://mlsanity.com/paper/2609.08609/) (2026)
- [Beyond frequency measures: Can contextual embeddings capture meaning change in scientific texts?](https://mlsanity.com/paper/2609.18804/) (2026)

## Abstract (arXiv)

Languages are continuously undergoing changes, and the mechanisms that underlie these changes are still a matter of debate. In this work, we approach language evolution through the lens of causality in order to model not only how various distributional factors associate with language change, but how they causally affect it. In particular, we study slang, which is an informal language that is typically restricted to a specific group or social setting. We analyze the semantic change and frequency shift of slang words and compare them to those of standard, nonslang words. With causal discovery and causal inference techniques, we measure the effect that word type (slang/nonslang) has on both semantic change and frequency shift, as well as its relationship to frequency, polysemy and part of speech. Our analysis provides some new insights in the study of language change, e.g., we show that slang words undergo less semantic change but tend to have larger frequency shifts over time.