# Analyzing Pok'emon and Mario Streamers' Twitch Chat with LLM-based User Embeddings

arXiv: https://arxiv.org/abs/2411.10934 · HTML: https://mlsanity.com/paper/2411.10934/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 17 Nov 2024.

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

- [Let's FACE it. Finnish Poetry Generation with Aesthetics and Framing](https://mlsanity.com/paper/1910.13946/) (2019)
- [From Plenipotentiary to Puddingless: Users and Uses of New Words in Early English Letters](https://mlsanity.com/paper/2103.09926/) (2021)

## Built on by (most cited first, 1 of 1)

- [Efficient Toxicity Detection in Gaming Chats: A Comparative Study of Embeddings, Fine-Tuned Transformers and LLMs](https://mlsanity.com/paper/2510.17924/) (2025)

## Abstract (arXiv)

We present a novel digital humanities method for representing our Twitch chatters as user embeddings created by a large language model (LLM). We cluster these embeddings automatically using affinity propagation and further narrow this clustering down through manual analysis. We analyze the chat of one stream by each Twitch streamer: SmallAnt, DougDoug and PointCrow. Our findings suggest that each streamer has their own type of chatters, however two categories emerge for all of the streamers: supportive viewers and emoji and reaction senders. Repetitive message spammers is a shared chatter category for two of the streamers.