Investigating the Variation of Formal and Informal Vocabulary in Pakistani English on Twitter

Abstract

This study investigates the vocabulary of the English language and its variation of formality and informality within a specified sample of tweets, focusing on the social modulation of linguistic forms in digital communication. Using a mixed-method approach, the study analyzes Twitter data through MAT tagger and corpus analysis tools. A pilot test was conducted to validate encoding using UTF-16 in Python due to Urdu script interference. Quantitative analysis employed a differential scale (20*–20) on both axes, while qualitative interpretation was based on Halliday’s (1960) Register Theory. Findings reveal that formality and informality vary situationally, shaped by social and linguistic constraints. The quantitative outcomes show distinguishable lexical shifts between explicit and situationally dependent expressions, indicating social media’s influence on language construction. The results also confirm that bilingual interference moderates linguistic variability within digital discourse. Future research should expand corpus diversity across platforms and demographics to refine linguistic dimensionality.