# Inducing lexicons of in-group language with socio-temporal context

arXiv: https://arxiv.org/abs/2409.19257 · HTML: https://mlsanity.com/paper/2409.19257/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 28 Sep 2024.

## Builds on (most cited first, 9 of 9)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Augmenting Neural Response Generation with Context-Aware Topical Attention](https://mlsanity.com/paper/1811.01063/) (2018)
- [From Dogwhistles to Bullhorns: Unveiling Coded Rhetoric with Language Models](https://mlsanity.com/paper/2305.17174/) (2023)
- [Characterizing English Variation across Social Media Communities with BERT](https://mlsanity.com/paper/2102.06820/) (2021)
- [Making "fetch" happen: The influence of social and linguistic context on nonstandard word growth and decline](https://mlsanity.com/paper/1802.04140/) (2018)
- [Building Knowledge-Guided Lexica to Model Cultural Variation](https://mlsanity.com/paper/2406.11622/) (2024)
- [The Road to Success: Assessing the Fate of Linguistic Innovations in Online Communities](https://mlsanity.com/paper/1806.05838/) (2018)
- [Identity Construction in a Misogynist Incels Forum](https://mlsanity.com/paper/2306.15745/) (2023)

## Built on by (most cited first, 2 of 2)

- [IYKYK: Using language models to decode extremist cryptolects](https://mlsanity.com/paper/2506.05635/) (2025)
- [Triggered: A Statistical Analysis of Environmental Influences on Extremist Groups](https://mlsanity.com/paper/2602.09289/) (2026)

## Abstract (arXiv)

In-group language is an important signifier of group dynamics. This paper proposes a novel method for inducing lexicons of in-group language, which incorporates its socio-temporal context. Existing methods for lexicon induction do not capture the evolving nature of in-group language, nor the social structure of the community. Using dynamic word and user embeddings trained on conversations from online anti-women communities, our approach outperforms prior methods for lexicon induction. We develop a test set for the task of lexicon induction and a new lexicon of manosphere language, validated by human experts, which quantifies the relevance of each term to a specific sub-community at a given point in time. Finally, we present novel insights on in-group language which illustrate the utility of this approach.