Corpus-Based Analysis of Lexical Variation in Pakistani Medical Blogs: Exploring Trends in Digital Health Communication

Abstract

This study investigates lexical variation in Pakistani medical blogs over a period of ten years (2015–2024) using a corpus-driven approach. Lexical variation is automatically measured using the web-based Lexical Complexity Analyzer developed by Lu and Ai (2010). In addition to indices like Type-Token Ratio (TTR), Mean Segmental TTR (MSTTR), and more advanced metrics like CTTR, RTTR, and the Uber Index, the lexical variation across the decade was further evaluated using verb-based variation indices (VV1, SVV1, CVV1) and the remaining five (LV, VV2, NV, ADJV, ADVV, MODV) estimate the lexical word variation within the examined corpus. The results show a moderate level of lexical variation, with some increase in vocabulary richness, particularly reflected in the Uber Index, which showed a gradual rise over time. Although TTR values remained fairly stable, the diversity of verb-based indices and other word categories remained limited, suggesting that while medical blogs evolve in content, they prioritize clarity and consistency in communicating healthcare information.