IMPACT OF SOCIAL MEDIA PLATFORM ON CODE SWITCHING FREQUENCY AND SENTIMENT POLARITY IN MULTILINGUAL USERS

Abstract

This research study examines impact of social media platforms on code-switching frequency and as well as sentiment polarity among multilingual users. Employing a mixed-methods approach, 1,200 social media posts taken from 60 different contributors across Twitter, Facebook, and Instagram were analyzed.  Using Natural Language Processing tools for automated sentiment analysis and also language identification, we quantified code switching patterns and emotional valence. Results exposed meaningfully higher code-switching frequencies in social media as compared to offline communication t (49)=6.78, p<.001), with code switched segments exhibiting additional optimistic sentiment than monolingual content (χ²(2)=28.74, p<.001). More in this research, qualitative findings suggest operators strategically employ code-switching for emotional expression and identity performance. The research donates to sociolinguistic sympathetic of digital communication by representative how platform affordances form polyglot performs, with implications for designing inclusive social media environments. These findings bridge computational linguistics based with discourse examination. This study will contribute new visions into emotional dimensions of language change in digital type spaces.