Community-Specific Slang and Entity Detection via Semantic Shift in Fine-Tuned Language Models
arXiv:2606.07522v1 Announce Type: cross 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 fi
This development leverages advancements in large language models to address a persistent challenge in understanding niche online communities, driven by the increasing volume and specificity of digital communication.
A strategic reader should care as this method offers a scalable way to extract deep semantic meaning from vast, unstructured community data, which is crucial for market intelligence, geopolitical analysis, and content moderation.
The ability to automatically identify and understand community-specific slang and entities through semantic shift analysis fundamentally changes how insights can be derived from online discourse, moving beyond manual qualitative analysis.
- · Social analytics platforms
- · Intelligence agencies
- · Marketing research firms
- · Content moderation companies
- · Traditional qualitative research methods
- · Generic sentiment analysis tools
Improved understanding of online subcultures and early trend detection.
Enhanced capabilities for targeted influence operations or commercial messaging within specific communities.
Potential for privacy concerns and misuse if applied to surveil or manipulate highly localized online groups.
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Read at arXiv cs.LG