From Pulpit to Platform: Algorithmic Mediation and the Transformation of Religious Authority on YouTube
Main Article Content
Abstract
Social media algorithms have been widely argued to shape information exposure through personalization based on users’ interaction histories, with potential implications for filter-bubble and echo-chamber dynamics. This study examined how algorithmic variables affected the relevance of YouTube content recommendations and considered broader socio-religious implications of increasingly personalized visibility. A quantitative quasi-experimental Interrupted Time Series Design (ITSD) was implemented by creating 25 test accounts with diverse demographic profiles and interest themes (informed by APJII 2024). Each account followed the same procedure across five iterations: a keyword search was conducted, the top-10 recommended videos were recorded, and three recommendations were opened using a randomized selection rule, yielding 1,250 video observations. Data was collected via the YouTube API, manually coded for recommendation relevance, transformed into numeric variables, and cleaned using the Interquartile Range (IQR) method. A logistic regression model was estimated and validated using the Hosmer–Lemeshow test, logit-linearity checks, and Variance Inflation Factor (VIF) diagnostics. Simple Exponential Smoothing (SES) and Holt’s Linear Trend were applied to project recommendation patterns across iterations. Iteration emerged as the most influential predictor of recommendation relevance, whereas other variables showed small or non-significant effects. The model demonstrated acceptable fit and no problematic multicollinearity, and forecasting suggested increasing relevance across iterations. Overall, the results were consistent with the strengthening of viewing-history-based personalization, which may reduce informational diversity and may facilitate a shift of religious authority toward digital actors more adaptive to algorithmic visibility
Article Details
License
Please find the rights and licenses in Profetik: Jurnal Komunikasi by submitting the article/manuscript of the article, the author(s) agree with this policy. No specific document sign-off is required.
1. License
The non-commercial use of the article will be governed by the Creative Commons Attribution license as currently displayed on Creative Commons Attribution-NonCommercial 4.0 International License
2. Author(s)' Warranties
The author warrants that the article is original, written by stated author(s), has not been published before, contains no unlawful statements, does not infringe the rights of others, is subject to copyright that is vested exclusively in the author and free of any third party rights, and that any necessary written permissions to quote from other sources have been obtained by the author(s).
3. User/Public Rights
Profetik's spirit is to disseminate articles published are as free as possible. Under the Creative Commons license, Profetik permits users to copy, distribute, display, and perform the work for non-commercial purposes only. Users will also need to attribute authors and Profetik on distributing works in the journal and other media of publications. Unless otherwise stated, the authors are public entities as soon as their articles got published.
4. Rights of Authors
Authors retain all their rights to the published works, such as (but not limited to) the following rights;
Copyright and other proprietary rights relating to the article, such as patent rights, The right to use the substance of the article in own future works, including lectures and books, The right to reproduce the article for own purposes, The right to self-archive the article (please read out deposit policy), The right to enter into separate, additional contractual arrangements for the non-exclusive distribution of the article's published version (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal (Profetik: Jurnal Komunikasi).
5. Co-Authorship
If the article was jointly prepared by more than one author, any authors submitting the manuscript warrants that he/she has been authorized by all co-authors to be agreed on this copyright and license notice (agreement) on their behalf, and agrees to inform his/her co-authors of the terms of this policy. Profetik will not be held liable for anything that may arise due to the author(s) internal dispute. Profetik will only communicate with the corresponding author.
6. Royalties
Being an open accessed journal and disseminating articles for free under the Creative Commons license term mentioned, author(s) aware that Profetik entitles the author(s) to no royalties or other fees.
7. Miscellaneous
Profetik will publish the article (or have it published) in the journal if the article’s editorial process is successfully completed. Profetik's editors may modify the article to a style of punctuation, spelling, capitalization, referencing and usage that deems appropriate. The author acknowledges that the article may be published so that it will be publicly accessible and such access will be free of charge for the readers as mentioned in point 3.
References
Airoldi, M. (2022). Machine habitus: Toward a sociology of algorithms. Cambridge Medford (Mass.): Polity press.
Allison, P. D. (2012). Logistic Regression Using SAS: Theory and Application (2nd ed.). SAS Press.
APJII. (2024). Survei Penetrasi Internet Indonesia 2024.
Barnett, V., & Lewis, T. (1994). Outliers in Statistical Data.
Bishop, S. (2018). Anxiety, panic and self-optimization: Inequalities and the YouTube algorithm. Convergence, 24(1), 69–84. https://doi.org/10.1177/1354856517736978
Bucher, T. (2012). Want to be on the top? Algorithmic power and the threat of invisibility on Facebook. New Media & Society, 14(7), 1164–1180. https://doi.org/10.1177/1461444812440159
Cook, T. D. ; C., D. T. (1979). Quasi-Experimentation: Design and Analysis Issues for Field Settings. Houghton Mifflin.
Covington, P., Adams, J., & Sargin, E. (2016, September). Deep neural networks for youtube recommendations. 191–198. Association for Computing Machinery, Inc. https://doi.org/10.1145/2959100.2959190
Creswell, J., W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. SAGE.
Davidson, J., Liebald, B., Liu, J., Nandy, P., & Van Vleet, T. (2010). The YouTube video recommendation system. 293–296. https://doi.org/10.1145/1864708.1864770
Delmonaco, D., Mayworm, S., Thach, H., Guberman, J., Augusta, A., & Haimson, O. L. (2024). “What are you doing, TikTok?”: How Marginalized Social Media Users Perceive, Theorize, and “Prove” Shadowbanning. Proceedings of the ACM on Human-Computer Interaction, 8(CSCW1). https://doi.org/10.1145/3637431
Dujeancourt, E., & Garz, M. (2023). The effects of algorithmic content selection on user engagement with news on Twitter. Information Society, 39(5), 263–281. https://doi.org/10.1080/01972243.2023.2230471
Engineering, T. (2023, March 31). Twitter’s recommendation algorithm. Retrieved from X Engineering Blog website: https://blog.x.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm
Eslami, M., Rickman, A., Vaccaro, K., Aleyasen, A., Vuong, A., Karahalios, K., … Sandvig, C. (2015). “I always assumed that I wasn’t really that close to [her]”: Reasoning about Invisible Algorithms in News Feeds. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 153–162. Seoul Republic of Korea: ACM. https://doi.org/10.1145/2702123.2702556
Farrar, D. E., & Glauber, R. R. (1967). Multicollinearity in Regression Analysis: The Problem Revisited. The Review of Economics and Statistics, 49(1), 92–107. https://doi.org/10.2307/1937887
Fourcade, M., & Johns, F. (2020). Loops, ladders and links: The recursivity of social and machine learning. Theory and Society, 49(5–6), 803–832. https://doi.org/10.1007/s11186-020-09409-x
Gillespie, T. (2014). The Relevance of Algorithms. In T. Gillespie, P. J. Boczkowski, & K. A. Foot (Eds.), Media Technologies: Essays on Communication, Materiality, and Society. The MIT Press. https://doi.org/10.7551/mitpress/9780262525374.001.0001
Griffiths, S., Harris, E. A., Whitehead, G., Angelopoulos, F., Stone, B., Grey, W., & Dennis, S. (2024). Does TikTok contribute to eating disorders? A comparison of the TikTok algorithms belonging to individuals with eating disorders versus healthy controls. Body Image, 51, 101807–101807. https://doi.org/10.1016/j.bodyim.2024.101807
Gujarati, D. N. (1978). Basic econometrics. McGraw-Hill.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed.). Pearson Prentice Hall.
Harambam, J., Helberger, N., & Van Hoboken, J. (2018). Democratizing algorithmic news recommendations: How to materialize voice in a technologically saturated media ecosystem. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133). https://doi.org/10.1098/rsta.2018.0088
Hindarto, I. H. (2022). INVESTIGATING HOW THE NATIONAL ONLINE MEDIA REPORTED THE LGBT COMMUNITY DURING THE COVID-19 PANDEMIC. Profetik: Jurnal Komunikasi, 15(2), 208–227. https://doi.org/10.14421/pjk.v15i2.2577
Hosmer Jr., D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression. John Wiley & Sons.
Hyndman, R.J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice (2nd ed.). OTexts.
Hyndman, Rob J., & Kostenko, A. V. (2007). Minimum Sample Size Requirements for Seasonal Forecasting Models. FORESIGHT, (6), 12–15.
Kingsley, S., Sinha, P., Wang, C., Eslami, M., & Hong, J. I. (2022). “Give Everybody [..] a Little Bit More Equity”: Content Creator Perspectives and Responses to the Algorithmic Demonetization of Content Associated with Disadvantaged Groups. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2). https://doi.org/10.1145/3555149
Krippendorff, K. (2019). Content Analysis: An Introduction to Its Methodology (Fourth). Los Angeles: SAGE Publications Ltd.
Larsson, A. O. (2020). Winning and losing on social media: Comparing viral political posts across platforms. Convergence, 26(3), 639–657. https://doi.org/10.1177/1354856518821589
Meta. (2025, June 11). Our approach to Facebook Feed ranking. Meta Transparency Center. Retrieved from https://transparency.meta.com/features/facebook-feed-ranking/
Narayanan, A. (2023). Understanding Social Media Recommendation Algorithms.
Nieborg, D. B., & Poell, T. (2018). The platformization of cultural production: Theorizing the contingent cultural commodity. New Media & Society, 20(11), 4275–4292. https://doi.org/10.1177/1461444818769694
Nisa, E. F. (2018). Social media and the birth of an Islamic social movement: ODOJ (One Day One Juz) in contemporary Indonesia. Indonesia and the Malay World, 46(134), 24–43. https://doi.org/10.1080/13639811.2017.1416758
Pariser, E. (2011). The Filter Bubble: What the Internet Is Hiding from You. Penguin Press.
Prianto, A. L., Abdillah, A., Syukri, S., Muhammad, F., & Yama, A. (2021). COMBATING INFODEMIC COVID-19: GOVERNMENT RESPONSE AGAINST FAKE NEWS ON SOCIAL MEDIA. Profetik: Jurnal Komunikasi, 14(2), 255–275. https://doi.org/10.14421/pjk.v14i2.2386
Rodilosso, E. (2024). Filter Bubbles and the Unfeeling: How AI for Social Media Can Foster Extremism and Polarization. Philosophy and Technology, 37(2). https://doi.org/10.1007/s13347-024-00758-4
Romele, A. (2023). Digital Habitus: A Critique of the Imaginaries of Artificial Intelligence (1st ed.). New York: Routledge. https://doi.org/10.4324/9781003400479
Saura, J. R., Palacios-Marqués, D., & Iturricha-Fernández, A. (2021). Ethical design in social media: Assessing the main performance measurements of user online behavior modification. Journal of Business Research, 129, 271–281. https://doi.org/10.1016/j.jbusres.2021.03.001
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference (Nachdr.). Belmont, CA: Wadsworth Cengage Learning.
Slama, M. (b). (2018). Practising Islam Through Social Media in Indonesia. Indonesia and the Malay World, 46(134), 1–4. https://doi.org/10.1080/13639811.2018.1416798
Solahudin, D., & Fakhruroji, M. (2019). Internet and Islamic Learning Practices in Indonesia: Social Media, Religious Populism, and Religious Authority. Religions, 11(1), 19. https://doi.org/10.3390/rel11010019
TikTok. (2020, June 18). How TikTok recommends videos #ForYou. Retrieved from https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you
Tukey, J. W. (1977). Exploratory Data Analysis. Addison-Wesley.
Wang, X., Zhang, Y., & Yamasaki, T. (2019, October). User-aware folk popularity rank: User-popularity-based tag recommendation that can enhance social popularity. 1970–1978. Association for Computing Machinery, Inc. https://doi.org/10.1145/3343031.3350920
Wulandari, T. D., & Nuraniwati, T. (2023). THE INVISIBLE HAND PROGRAM BY PT PERTAMINA MOR IV TO EDUCATE AND INCREASE PUBLIC AWARENESS ON SUBSIDIZED 3KG LPG. Profetik: Jurnal Komunikasi, 16(1), 123–138. https://doi.org/10.14421/pjk.v16i1.2277
Article Sidebar
Download Full Text
PDF