International Journal of Scientific Research and Engineering Development

This research examines the growing semantic dilution of technical buzzwords such as “blockchain,” “AI,” and “GPT,” where precise concepts gradually become vague due to widespread digital usage across platforms like LinkedIn, Twitter, Reddit, and blogs. The study proposes an unsupervised SBERT (allMiniLM-L6-v2)–based framework to measure semantic change using semantic trajectory divergence (σ) and a Dilution Score derived from cosine similarity decay over time. By analysing a longitudinal dataset (2020–2025), the framework enables real-time evaluation of technical fidelity and evolving language patterns in online discourse.

Shifa Bilal Tamboli, Simeen Phiroj Mulani, Arman Tajuddin Shiakh,"SBERT-Driven Semantic Trajectory Analysis for Quantifying Technical Buzzword Dilution" International Journal of Scientific Research and Engineering Development, V9(2): Page(2537-2543) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.