Polarization on YouTube: Collaborative Interaction and the Appeal of Strong Discourses and Monetization – A Proposal for an Online Tool
Abstract
Social media platforms like YouTube encourage user collaboration through interactions, algorithmic recommendations, and monetization systems, creating an environment conducive to the amplification of polarized and highly engaging discourse. Using public data, we propose a Web Dashboard that automatically collects data (RPA) to analyze the correlation between engagement, monetization, and toxicity on YouTube. By examining how user collaboration drives intense discourse and impacts content moderation, this work contributes to the broader challenges of digital governance and the influence of algorithms in shaping narratives and discourse. Contributions include widgets, charts, tables, and automatic report generation, providing valuable insights for scholars of digital polarization.References
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Bartl, M. (2018). Youtube channels, uploads and views: A statistical analysis of the past 10 years. Convergence, 24(1):16–32.
Covington, P., Adams, J., and Sargin, E. (2016). Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys ’16), pages 191–198, New York, NY, USA. Association for Computing Machinery.
Guimaraes, S. S., Reis, J. C., Ribeiro, F. N., and Benevenuto, F. (2020). Characterizing toxicity on facebook comments in brazil. In Proceedings of the Brazilian Symposium on Multimedia and the Web, pages 253–260.
Hua, Y., Horta Ribeiro, M., Ristenpart, T., West, R., and Naaman, M. (2022). Characterizing alternative monetization strategies on youtube. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2):1–30.
Mariconti, E., Suarez-Tangil, G., Blackburn, J., De Cristofaro, E., Kourtellis, N., Leontiadis, I., Serrano, J. L., and Stringhini, G. (2019). ‘you know what to do‘ proactive detection of youtube videos targeted by coordinated hate attacks. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW):1–21.
Nogara, G., Vishnuprasad, P. S., Cardoso, F., Ayoub, O., Giordano, S., and Luceri, L. (2022). The disinformation dozen: An exploratory analysis of covid-19 disinformation proliferation on twitter. In Proceedings of the 14th ACM web science conference 2022, pages 348–358.
Obadimu, A., Mead, E., Hussain, M. N., and Agarwal, N. (2019). Identifying toxicity within youtube video comment. In Social, Cultural, and Behavioral Modeling: 12th International Conference, SBP-BRiMS 2019, pages 214–223. Springer International Publishing.
Pariser, E. (2011). The Filter Bubble: What the Internet is Hiding from You. Penguin UK.
Pereira, E. V., Melo, P., Junior, M., Mafra, V. O., Reis, J. C., and Benevenuto, F. (2022). Analyzing youtube videos shared on whatsapp and telegram political public groups. In Proceedings of the Brazilian Symposium on Multimedia and the Web, pages 28–37.
Rieder, B., Borra, E., Òscar Coromina, and Matamoros-Fernández, A. (2023). Making a living in the creator economy: A large-scale study of linking on youtube. Social Media+ Society, 9(2):20563051231180628.
Rieder, B., Òscar Coromina, and Matamoros-Fernández, A. (2020). Mapping youtube: A quantitative exploration of a platformed media system. First Monday, 25(8):Article number: 10667.
Stevens, F., Nurse, J. R. C., and Arief, B. (2021). Cyber stalking, cyber harassment, and adult mental health: A systematic review. Cyberpsychology, Behavior, and Social Networking, 24(6):367–376. Epub 2020 Nov 12.
Velho, R. M., Mendes, A. M. F., and Azevedo, C. L. N. (2020). Communicating science with youtube videos: How nine factors relate to and affect video views. Frontiers in Communication, 5:567606.
Zappin, A., Malik, H., Shakshuki, E. M., and Dampier, D. A. (2022). Youtube monetization and censorship by proxy: A machine learning prospective. Procedia Computer Science, 198:23–32.
Zhou, J., Zhang, Y., Wang, Q., and Zhu, Y. (2022). The impact of youtube recommendation system on video views. Computers in Human Behavior, 129:107123.
Published
2025-06-02
How to Cite
ALMEIDA, Luis Gustavo; GARCIA, Ana Cristina B.; SIMÕES, Jefferson Elbert.
Polarization on YouTube: Collaborative Interaction and the Appeal of Strong Discourses and Monetization – A Proposal for an Online Tool. In: BRAZILIAN SYMPOSIUM ON COLLABORATIVE SYSTEMS (SBSC), 20. , 2025, Manaus/AM.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 127-138.
ISSN 2326-2842.
DOI: https://doi.org/10.5753/sbsc.2025.8509.
