Algorithmic visibility and linguistic inequality: how social media platforms amplify Indonesian and marginalize regional languages - Daroji (CV Narasi Khatulistiwa Indonesia, 2026)
Background: Social media platforms increasingly mediate linguistic visibility, yet numerical presence alone cannot reveal whether regional languages receive comparable exposure, functional diversity, or infrastructural support within multilingual digital environments. Objective: This study examines how YouTube structures the visibility of Indonesian, Javanese, and Madurese content through differences in retrieval, thematic distribution, and dominant-language mediation. Method: Using a sequential mixed-method platform-audit design, the study combines a 30-video discovery corpus with controlled visibility auditing, stratified quantitative content analysis, and sociolinguistic domain analysis across six predefined thematic categories. Results: The corpus was numerically balanced across the three languages, but its thematic composition and verification readiness differed substantially. Javanese covered all six thematic categories, Indonesian five, and Madurese four, with Madurese candidates concentrated in humour and news. Linguistic evidence was strongest for Madurese, while Indonesian was frequently inferred as an unmarked default and regional-language content sometimes relied on Indonesian subtitles or translations for wider accessibility. Implication: The findings suggest that platform-based linguistic inequality is based on the interaction of ranked exposure, functional-domain breadth, evidential certainty, and dominant-language mediation rather than as a simple disparity in content counts, thereby. Novelty: The novelty of this study is a replicable framework for examining multilingual visibility without attributing observed differences prematurely to intentional algorithmic suppression at platform scale.