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.