The rapid growth of scientific publishing creates both opportunities and challenges for understanding emerging research trends. Traditional approaches to trend detection, such as citation analyses or latent topic models, often lag behind real developments or lack interpretability. To address these limitations, this study proposes a lightweight and transparent methodology for detecting and evaluating emerging scientific concepts through temporal n -gram analysis. From the OpenAlex corpus of scholarly metadata, n -grams are extracted from titles and abstracts, and aggregated into quarterly frequency series. Bursty dynamics are identified using complementary detection algorithms, including Kleinberg’s state-machine model and a moving average convergence divergence (MACD) approach. Evaluation uses a human-centered framework in which participants judge temporal plots of anonymized n -grams, providing both pairwise comparisons and graded burstiness scores. These judgments are compared with algorithmic rankings, and alignment between large language models (LLMs) and human raters is examined. We further contrast burst dynamics across fast- and slow-evolving subfields and verify the robustness of the approach to corpus-growth effects through a normalization experiment. By integrating scalable temporal analysis and multi-perspective evaluation, this work contributes a framework for monitoring and characterizing the trajectory of emerging research topics. The approach is intended as an interpretable tool for digital libraries, funding agencies, and research analysts seeking to navigate fast-evolving scientific landscapes.