UNVEILING DIGITAL DISCOURSES: A SMILE-BASED SOMELIT ANALYSIS OF LINGUISTIC STRATEGIES IN SOCIAL MEDIA COMMUNICATION

Abstract

The rapid proliferation of digital communication has reshaped how individuals engage with language, creating a dynamic interface between linguistics and communication, forming a rich area for academic inquiry. This study employs the SoMeLit (2024) (Social-Media Literacy Interface Tool) mechanism, integrating Schreurs and

Vandenbosch’s (2020) Social Media Literacy (SMILE) framework to explore the interplay between linguistic strategies and communicative practices on social media platforms. For the said purpose, the present research focuses on different public digital platforms i.e., WhatsApp, Twitter, TikTok and Instagram interactions, the research examines how language constructs identities, fosters community, and negotiates power dynamics. Multimodal elements, such as hashtags activism, conversational threads, emojis, meme culture and textual structures, are analyzed to highlight the convergence of visual, verbal, and contextual cues in digital discourse to uncover how positivity bias informs digital discourse. Employing a mixed-methods approach, the study examines both quantitative trends and qualitative subtleties in user interactions. Findings reveal the adaptability of linguistic patterns in navigating positivity bias and emphasize the role of immediacy and interactivity in shaping modern communication. Thus, this research contributes to understanding the transformative potential of social media literacy in decoding communication strategies and redefining traditional linguistic norms.