A Corpus-Based Analysis of Lexical Characteristics Across English News Categories for L2 Pedagogical Use

News articles are widely regarded as valuable resources for vocabulary acquisition. However, they encompass diverse categories, each catering to specific learner needs. This study analysed the vocabulary of 3,000 news articles across 12 categories, focusing on lexical profiling, lexical level, variation, density, and CEFR level to support L2 learners. The results showed that the Health category had the highest General Service List word coverage (81.01%), while Technology featured the most Academic Word List terms (8.23%). Fashion contained the largest proportion of specialised vocabulary (18.31%) and exhibited the highest lexical variation (51.29%). High-frequency words dominated all categories (91–94.79%), while Fashion included the most mid-frequency (5.84%) and low-frequency (2.36%) words. Lexical density was highest in the Environment category (57.85%) and lowest in Sports (53.2%). The CEFR analysis indicated that A1 and A2 words comprised the majority (76.66% and 10.44%, respectively), while categories such as Fashion and Nutrition included the highest proportions of C1-C2 words (6.32% and 6.53%, respectively). These findings suggest that categories such as Health and Sports are suitable for beginner learners, while Fashion and Nutrition offer more complex vocabulary for advanced learners. This study highlights the unique lexical characteristics of news categories, providing educators and learners with guidance on selecting authentic materials to enhance vocabulary learning.