Algorithmic visibility and linguistic inequality: how social media platforms amplify Indonesian and marginalize regional languages

Daroji

doi:10.67490/ijlc.v1i2.885

Authors

Daroji Universitas Sarjanawiyata Tamansiswa Author

DOI:

https://doi.org/10.67490/ijlc.v1i2.885

Keywords:

algorithmic visibility, linguistic inequality, regional languages, YouTube

Abstract

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.

References

[1] T. Bucher, If ... then: Algorithmic power and politics. Oxford University Press, 2018.

[2] B. Schneider, “Multilingualism and AI: The regimentation of language in the age of digital capitalism,” Signs and Society, vol. 10, no. 3, pp. 362–387, 2022, doi: 10.1086/721757.

[3] K. Cotter, “Playing the visibility game: How digital influencers and algorithms negotiate influence on Instagram,” New Media & Society, vol. 21, no. 4, pp. 895–913, 2019, doi: 10.1177/1461444818815684.

[4] B. E. Duffy and C. Meisner, “Platform governance at the margins: Social media creators’ experiences with algorithmic (in)visibility,” Media, Culture & Society, vol. 45, no. 2, pp. 285–304, 2023, doi: 10.1177/01634437221111923.

[5] D. E. Blasi, A. Anastasopoulos, and G. Neubig, “Systematic inequalities in language technology performance across the world’s languages,” presented at the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Volume 1, Long Papers, Association for Computational Linguistics, 2022, pp. 5486–5505. doi: 10.18653/v1/2022.acl-long.376.

[6] L. Song, X. Hu, G. Zhang, P. Spachos, K. N. Plataniotis, and H. Wu, “Networking systems of AI: On the convergence of computing and communications,” IEEE Internet of Things Journal, vol. 9, no. 20, pp. 20352–20381, 2022.

[7] A. Fawaid, Who Controls the Narrative? Language, Media, and Politics of Representation. Probolinggo: Pustaka Nurja, 2026.

[8] N. H. Mazlan, C. W. Putra, and H. Sulistyo, “Understanding reader navigation patterns in multi-path hypertext fiction: A case study approach to Patchwork Girl,” lingtech, vol. 1, no. 2, pp. 1–12, Jun. 2025, doi: 10.64595/lingtech.v1i1.3.

[9] M. R. M. Nor and A. Zubaidi, “Digital reading platforms in literary learning: cultural understanding and engagement in Indonesian and Malay texts,” Lingua Technica: Journal of Digital Literary Studies, vol. 2, no. 1, pp. 33–50, 2026, doi: 10.64595/lingtech.v2i1.131.

[10] J. Leung, “Hurdles and horizons of linguistics for social justice,” Language in Society, vol. 52, pp. 882–893, Nov. 2023, doi: 10.1017/s0047404523000635.

[11] T. Gillespie, “The relevance of algorithms,” in Media technologies: Essays on communication, materiality, and society, T. Gillespie, P. J. Boczkowski, and K. A. Foot, Eds., MIT Press, 2014, pp. 167–194.

[12] R. Kitchin, “Thinking critically about and researching algorithms,” Information, Communication & Society, vol. 20, no. 1, pp. 14–29, 2017, doi: 10.1080/1369118X.2016.1154087.

[13] T. Bucher, “Want to be on the top? Algorithmic power and the threat of invisibility on Facebook,” New Media & Society, vol. 14, no. 7, pp. 1164–1180, 2012, doi: 10.1177/1461444812440159.

[14] S. Bishop, “Managing visibility on YouTube through algorithmic gossip,” New Media & Society, vol. 21, no. 11–12, pp. 2589–2606, 2019, doi: 10.1177/1461444819854731.

[15] J. A. Fishman, Reversing language shift: Theoretical and empirical foundations of assistance to threatened languages. Multilingual Matters, 1991.

[16] UNESCO Ad Hoc Expert Group on Endangered Languages, “Language vitality and endangerment,” UNESCO, 2003.

[17] P. Joshi, S. Santy, A. Budhiraja, K. Bali, and M. Choudhury, “The state and fate of linguistic diversity and inclusion in the NLP world,” presented at the Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, 2020, pp. 6282–6293. doi: 10.18653/v1/2020.acl-main.560.

[18] A. Grützner-Zahn, “Surveying the technology support of languages,” presented at the Proceedings of the Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages, Association for Computational Linguistics, 2024.

[19] H. Kelly-Holmes, “Multilingualism and technology: A review of developments in digital communication from monolingualism to idiolingualism,” Annual Review of Applied Linguistics, vol. 39, pp. 24–39, 2019, doi: 10.1017/S0267190519000102.

[20] R. W. Ikomah and Z. H. Sain, “Text mining and semantic modeling of literary corpora: a machine learning–based study of Indonesian fiction,” Lingua Technica: Journal of Digital Literary Studies, vol. 2, no. 1, pp. 51–67, 2026, doi: 10.64595/lingtech.v2i1.133.

[21] E. Harmer and R. Southern, “Twitter to Yell at Them, Facebook to Ask Questions and Instagram to Show Support? Exploring Platform Effects in Responses to Women Politicians On Social Media,” Political Communication, pp. 1–21, Nov. 2025, doi: 10.1080/10584609.2025.2585498.

[22] K. Krippendorff, “Reliability in content analysis: Some common misconceptions and recommendations,” Human Communication Research, vol. 30, no. 3, pp. 411–433, 2004, doi: 10.1111/j.1468-2958.2004.tb00738.x.

[23] B. Duffy and C. Meisner, “Platform governance at the margins: Social media creators’ experiences with algorithmic (in)visibility,” Media, Culture & Society, vol. 45, pp. 285–304, Jul. 2022, doi: 10.1177/01634437221111923.

[24] A. Fawaid, “Mapping the field of digital literary studies: Concepts, methods, and emerging directions,” Lingua Technica: Journal of Digital Literary Studies, vol. 1, no. 1, pp. 1–13, Jan. 2025, doi: 10.64595/99c6t274.

[25] R. Assyabani, “Multimodal poetics in digital literature: A corpus-based analysis of visual-verbal design, screen-based textuality, and reader interaction,” Lingua Technica: Journal of Digital Literary Studies, vol. 1, no. 1, pp. 41–53, Jan. 2025, doi: 10.64595/cy0q2n14.