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.

翻译：我们提出了一种新颖的数字人文方法，通过大型语言模型（LLM）生成的用户嵌入来表征Twitch聊天参与者。我们使用亲和传播算法对这些嵌入进行自动聚类，并通过人工分析进一步细化聚类结果。本文选取三位Twitch主播（SmallAnt、DougDoug与PointCrow）各自的单次直播聊天记录进行分析。研究发现：每位主播拥有独特的观众群体类型，但所有主播的观众中均存在两类共性群体——支持型观众与表情/反应型观众。其中两位主播的聊天室还存在重复消息刷屏者这一共有观众类别。