Community-Specific Slang and Entity Detection via Semantic Shift in Fine-Tuned Language Models

Julia Kruk; Sanchita Porwal; Amitrajit Bhattacharjee; Mansi Phute

cs.CLApr 19, 2026

Community-Specific Slang and Entity Detection via Semantic Shift in Fine-Tuned Language Models

Authors: Julia Kruk, Sanchita Porwal, Amitrajit Bhattacharjee, Mansi Phute

Organizations: Georgia Institute of Technology College of Computing Atlanta, GA, USA

Abstract

We propose an unsupervised method of resolving slang, unique entities, and folklore from online communities by isolating words in the lexicon that have the highest magnitude of semantic shift. Semantic shift is defined as the evolution of a word's encoded representation as a result of fine-tuning a pretrained Large Language Model (LLM) on a community-specific text corpus. This value is inversely proportional to the cosine similarity between the base model's encoded representation of a word, and a fine-tuned model's encoded representation. We fine-tune the DistilRoBERTa model on text corpora collected from 3 Reddit subreddits (r/Technology, r/Gaming, r/WorldofWarcraft), model a distribution of cosine similarity over the lexicon, and show that one can successfully resolve words that have unique significance to the community by pulling data in the bottom 10-percentile. In contrast, we show that data in the top 10-percentile consist of words that carry relatively universal semantics.

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Sep 22, 2026 · cs.CLJ/K move · Enter open · S save

From Utterances to Networks: Modelling Slang Adoption and Diffusion Across Subreddits

Xiaoning Wang, Ted Underwood, Zhewei Sun

Adoption and diffusion of neologisms in online communities have received renewed attention in recent years. As internet slang terms such as APT, referring to a K-pop song, and phrases such as Canon Event meaning an embarrassing but pivotal event, go viral online, it becomes increasingly important to understand the mechanisms that contribute to their success. Prior studies have often explained slang diffusion either from the perspective of social interaction or from the linguistic properties of the slang itself, but rarely from both perspectives together. One major obstacle has been the high cost of annotating slang usage in large-scale online communication. Recent advances in large language models (LLMs), however, make it possible to use them as scalable annotators for such tasks. In this study, we first curate a human-annotated benchmark to evaluate LLM performance in detecting slang usage in real Reddit communication. We then leverage LLM-based annotations to model slang adoption and diffusion. Our results show that slang diffusers with higher bridging capital are associated with increased subsequent adoption, whereas diffusers with higher bonding capital are associated with reduced adoption. We also find that wider contextual usage of a slang term is associated with a longer time before new users officially adopt it. Together, these findings suggest that both social-network structure and linguistic context shape the diffusion of neologisms in online communities.

Social Media Linguistics

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Abstract

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