Authors: Poonam Mishra, Neeraj Gupta
Abstract: The rapid acceleration of scientific publication rates has created unprecedented challenges in tracking the evolution of research trends and identifying emerging paradigms within academic disciplines. This paper presents a novel computational framework for temporal trend evolution mapping in scientific literature that combines advanced natural language processing techniques with dynamic network analysis to capture and visualize the progression of scientific concepts over time. Our methodology integrates transformer-based document embeddings, temporal clustering algorithms, and graph-based trend propagation models to create comprehensive maps of knowledge evolution across multiple time scales. The framework employs a multi-dimensional approach that analyzes citation patterns, semantic similarity evolution, and author collaboration networks to identify trend emergence, maturation, and decline phases. Experimental validation on large-scale datasets from PubMed, arXiv, and Web of Science demonstrates the framework's effectiveness in detecting significant research trends up to 18 months before they become mainstream, with precision scores exceeding 0.89 for trend prediction tasks. The system successfully identified the emergence of CRISPR gene editing research, COVID-19 therapeutic developments, and artificial intelligence applications in drug discovery as major trending topics months before their widespread recognition. Our contribution provides researchers, funding agencies, and academic institutions with powerful tools for strategic research planning, early trend identification, and comprehensive understanding of scientific knowledge evolution patterns.
DOI: http://doi.org/10.5281/zenodo.17099295
