Algospeak: The Evolution of Language in the Age of Social Media in Indonesia: Algospeak: Evolusi Bahasa di Era Media Sosial di Indonesia - Lamsike Pateda; Ibnu Rawandhy N. Hula; Ana Mariana (ELOQUENCE : Journal of Foreign Language, 2025)
Background: The rapid expansion of digital platforms has given rise to new linguistic practices, including algospeak, a creative strategy used by users to circumvent algorithmic moderation. Purpose: This study examines the morphological patterns, social functions, and broader implications of algospeak in Indonesian social media. Method: Employing a qualitative descriptive approach with a digital sociolinguistic case study design, the research analysed 500 posts collected purposively from Twitter (X), TikTok, and Instagram during 2023–2025. Data were gathered through digital documentation and screened based on linguistic relevance. Analytical procedures combined morphological analysis, sociolinguistic interpretation, and critical discourse analysis, while validity was ensured through triangulation of sources and theories, peer debriefing, and limited member checking. Results and Discussion: The findings reveal four dominant morphological strategies: phonological/orthographic substitution, abbreviation and acronyms, blending and hybridisation, and euphemism/metaphor. These patterns demonstrate that algospeak functions not only as a technical tactic to avoid censorship but also as a form of humour, solidarity, and community identity. Beyond describing linguistic forms, the study contributes to bridging a gap in digital sociolinguistics by highlighting a non-Western perspective and showing how morphology operates as a site of resistance and adaptation to algorithmic control. Conclusions and Implications: algospeak reflects the dynamic interaction between human creativity and technological governance, underscoring the need to rethink keyword-based moderation and integrate digital language practices into literacy and education. Future research should expand toward multimodal analysis and cross-linguistic comparison to better capture the global evolution of language in algorithm-driven environments.