Visibility by design: Recommendation algorithms and the unequal circulation of Indonesian linguistic content on YouTube

Wahfiuddin Rahmad Harahap

doi:10.67490/ijlc.v1i3.1014

Authors

Wahfiuddin Rahmad Harahap Universitas Islam Negeri Raden Fatah Palembang Author

DOI:

https://doi.org/10.67490/ijlc.v1i3.1014

Keywords:

Algorithmic visibility, Indonesian languages, linguistic inequality, platform legibility, YouTube recommendation

Abstract

Background: YouTube has become a major infrastructure for Indonesian linguistic circulation, yet its recommendation pathways may distribute visibility unevenly across standard Indonesian, regional languages, dialectal varieties, and multilingual content. Objective: This study examines how recommendation architecture, linguistic hierarchy, and platform legibility shape the unequal circulation of Indonesian linguistic content on YouTube. Method: Using a qualitative-dominant mixed platform audit, this study analyzes 33 public YouTube videos, 10 scholarly sources, and seven contextual documents through visibility-pathway auditing, linguistic drift mapping, and metadata–multimodal legibility analysis. Results: Standard Indonesian and multilingual Indonesian content occupy stronger visibility positions because their titles, institutions, and audience cues align with national, educational, and transnational categories. Regional and minority-language content remains present but circulates more often through local, heritage, preservation, or weakly stabilized recommendation pathways. Recommendation drift further shows that several regional-language pathways move toward standard Indonesian, adjacent cultural content, or ambiguous clusters rather than sustaining linguistic continuity. Implication: These findings indicate that digital linguistic inequality should be examined through ranking, recurrence, metadata, and recommendation movement, not merely through online presence. Novelty: This study advances Indonesian sociolinguistic platform research by integrating recommendation auditing, language ideology, and multimodal platform-legibility analysis within one empirical framework for studying algorithmic linguistic visibility today

References

[1] J. Kaiser and A. Rauchfleisch, "Birds of a feather get recommended together: Algorithmic homophily in YouTube’s channel recommendations in the United States and Germany," Social Media + Society, vol. 6, 2020.

[2] B. Kirdemir, J. Kready, E. Mead, M. N. Hussain, and N. Agarwal, "Examining video recommendation bias on YouTube," in Lecture Notes in Computer Science, 2021, pp. 106-116.

[3] B. Kirdemir, J. Kready, E. Mead, M. N. Hussain, N. Agarwal, and D. Adjeroh, "Assessing bias in YouTube’s video recommendation algorithm in a cross-lingual and cross-topical context," in Lecture Notes in Computer Science, 2021, pp. 71-80.

[4] B. Kirdemir and N. Agarwal, "Exploring bias and information bubbles in YouTube’s video recommendation networks," in Lecture Notes in Computer Science, 2022, pp. 166-177.

[5] U. Onyepunuka, M. Alassad, L. Nwana, and N. Agarwal, "Multilingual analysis of YouTube’s recommendation system: Examining topic and emotion drift in the “Cheng Ho” narrative," 2023, pp. 25-35.

[6] M. Haroon, M. E. Wojcieszak, A. Chhabra, X. Liu, P. Mohapatra, and Z. Shafiq, "Auditing YouTube’s recommendation system for ideologically congenial, extreme, and problematic recommendations," Proceedings of the National Academy of Sciences of the United States of America, vol. 120, 2023.

[7] H. Hosseinmardi, A. Ghasemian, M. Rivera-Lanas, M. H. Ribeiro, R. West, and D. Watts, "Causally estimating the effect of YouTube’s recommender system using counterfactual bots," Proceedings of the National Academy of Sciences of the United States of America, vol. 121, 2023.

[8] X. Yu, M. Haroon, E. Menchen-Trevino, and M. Wojcieszak, "Nudging recommendation algorithms increases news consumption and diversity on YouTube," PNAS Nexus, vol. 3, 2024.

[9] M. C. Cakmak, N. Agarwal, and R. Oni, "The bias beneath: Analyzing drift in YouTube’s algorithmic recommendations," Social Network Analysis and Mining, vol. 14, 2024.

[10] M. C. Cakmak and N. Agarwal, "Influence of symbolic content on recommendation bias: Analyzing YouTube’s algorithm during Taiwan’s 2024 election," Applied Network Science, vol. 10, 2025.

[11] F. Wasiqah, "Language ideologies and media representation: Framing Madurese dialects in Indonesian television news," Indonesian Journal of Language and Communication Studies, vol. 1, no. 1, pp. 22-30, 2026.

[12] E. F. Hastuti, "Digital rhetoric in Indonesian political campaigns: A multimodal discourse analysis of social media narratives," Indonesian Journal of Language and Communication Studies, vol. 1, no. 1, pp. 1-11, 2026.

[13] Daroji, "Algorithmic visibility and linguistic inequality: How social media platforms amplify Indonesian and marginalize regional languages," Indonesian Journal of Language and Communication Studies, vol. 1, no. 2, pp. 60-72, 2026.

[14] R. Pahlawan, "From moderation to marginalization: Language, power, and content governance on Indonesian social media platforms," Indonesian Journal of Language and Communication Studies, vol. 1, no. 2, pp. 101-114, 2026.

[15] M. Yesilada and S. Lewandowsky, "Systematic review: YouTube recommendations and problematic content," Internet Policy Review, vol. 11, 2022.

[16] S. Dagtas, M. C. Cakmak, and N. Agarwal, "Efficient data retrieval and comparative bias analysis of recommendation algorithms for YouTube Shorts and long-form videos," arXiv, 2025.

[17] W. S. P. Hapsari and A. V. Sufianti, "Reclaiming indigenous voices: Linguistic landscape of minority languages in urban public spaces in Indonesia," Indonesian Journal of Language and Communication Studies, vol. 1, no. 1, pp. 40-49, 2026.

[18] U. F. S. Faradis, "The role of local language in risk communication: A case study of disaster messaging in Eastern Indonesia," Indonesian Journal of Language and Communication Studies, vol. 1, no. 1, pp. 12-21, 2026.

[19] A. C. Kartika, "Crisis communication and language strategy: A corpus-based study of governmental Covid-19 briefings in Indonesia," Indonesian Journal of Language and Communication Studies, vol. 1, no. 1, pp. 50-59, 2026.

[20] Rohmadi, "Cross-cultural pragmatics in e-commerce: A comparative study of buyer-seller interactions in Indonesia and Malaysia," Indonesian Journal of Language and Communication Studies, vol. 1, no. 1, pp. 31-39, 2026.

[21] H. Aini, "Automating intercultural communication: Pragmatic failures and cultural bias in AI-mediated Indonesian-English translation," Indonesian Journal of Language and Communication Studies, vol. 1, no. 2, pp. 87-100, 2026.

[22] K. Qian and S. Jain, "Digital content creation: An analysis of the impact of recommendation systems," Management Science, vol. 70, pp. 8668-8684, 2024.

[23] N. Albadi, M. Kurdi, and S. Mishra, "Deradicalizing YouTube: Characterization, detection, and personalization of religiously intolerant Arabic videos," Proceedings of the ACM on Human-Computer Interaction, vol. 6, pp. 1-25, 2022.

[24] A. Fenomena, P. Bahasa, I. Oleh, K. Kreator, Y. Asing, and T. M. Pandiangan, "Analisis fenomena penuturan bahasa Indonesia oleh konten kreator YouTube asing," Jurnal Pendidikan, Bahasa dan Budaya, 2024.

[25] M. Dewi, "Human or machine? Discursive constructions of authenticity in AI-generated news and public communication in Indonesia," Indonesian Journal of Language and Communication Studies, vol. 1, no. 2, pp. 73-86, 2026.

[26] K. M. A. A. Z. Kanakkarasan, "Synthetic voices and mediated trust: A multimodal analysis of AI-generated political communication in Indonesia," Indonesian Journal of Language and Communication Studies, vol. 1, no. 2, pp. 115-128, 2026.