# Characterizing the Language of Online Communities and its Relation to Community Reception

arXiv: https://arxiv.org/abs/1609.04779 · HTML: https://mlsanity.com/paper/1609.04779/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2016.

## Built on by (most cited first, 4 of 4)

- [Prototypical Human-AI Collaboration Behaviors from LLM-Assisted Writing in the Wild](https://mlsanity.com/paper/2505.16023/) (2025)
- [Quantifying How Hateful Communities Radicalize Online Users](https://mlsanity.com/paper/2209.08697/) (2022)
- [Do Large Language Models Adapt to Language Variation across Socioeconomic Status?](https://mlsanity.com/paper/2602.11939/) (2026)
- [What Software Engineering Looks Like to AI Agents? -- An Empirical Study of AI-Only Technical Discourse on MoltBook](https://mlsanity.com/paper/2605.08380/) (2026)

## Abstract (arXiv)

This work investigates style and topic aspects of language in online communities: looking at both utility as an identifier of the community and correlation with community reception of content. Style is characterized using a hybrid word and part-of-speech tag n-gram language model, while topic is represented using Latent Dirichlet Allocation. Experiments with several Reddit forums show that style is a better indicator of community identity than topic, even for communities organized around specific topics. Further, there is a positive correlation between the community reception to a contribution and the style similarity to that community, but not so for topic similarity.