# Style Matters! Investigating Linguistic Style in Online Communities

arXiv: https://arxiv.org/abs/2209.13114 · HTML: https://mlsanity.com/paper/2209.13114/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 27 Sep 2022.

## Builds on (most cited first, 2 of 2)

- [A Stylometric Inquiry into Hyperpartisan and Fake News](https://mlsanity.com/paper/1702.05638/) (2017)
- [What the Language You Tweet Says About Your Occupation](https://mlsanity.com/paper/1701.06233/) (2017)

## Built on by (most cited first, 3 of 3)

- [Misinformation is not about Bad Facts: An Analysis of the Production and Consumption of Fringe Content](https://mlsanity.com/paper/2403.08391/) (2024)
- [Can You Make It Sound Like You? Post-Editing LLM-Generated Text for Personal Style](https://mlsanity.com/paper/2604.24444/) (2026)
- [Measuring the Semantic Structure and Evolution of Conspiracy Theories](https://mlsanity.com/paper/2603.26062/) (2026)

## Abstract (arXiv)

Content has historically been the primary lens used to study language in online communities. This paper instead focuses on the linguistic style of communities. While we know that individuals have distinguishable styles, here we ask whether communities have distinguishable styles. Additionally, while prior work has relied on a narrow definition of style, we employ a broad definition involving 262 features to analyze the linguistic style of 9 online communities from 3 social media platforms discussing politics, television and travel. We find that communities indeed have distinct styles. Also, style is an excellent predictor of group membership (F-score 0.952 and Accuracy 96.09%). While on average it is statistically equivalent to predictions using content alone, it is more resilient to reductions in training data.