Polarização no YouTube: Interação Colaborativa e a Atração dos Discursos Fortes e Monetização - Proposta de uma Ferramenta Online
Resumo
As plataformas de redes sociais, como o YouTube, incentivam a colaboração entre usuários por meio de interações, recomendações algorítmicas e sistemas de monetização, criando um ambiente propício para a amplificação de discursos polarizados e altamente engajadores. Utilizando dados públicos, propomos um Dashboard Web que coleta dados automaticamente (RPA) para analisar a correlação entre engajamento, monetização e toxicidade no YouTube. Ao analisar como a colaboração dos usuários impulsiona discursos intensos e impacta a moderação de conteúdo, este trabalho contribui para os desafios da governança digital e a influência dos algoritmos na formação de discursos e narrativas. As contribuições incluem widgets, gráficos, tabelas e geração automática de relatórios, fornecendo informações para estudiosos da polarização digital.Referências
Andres, R., Rossi, M., and Tremblay, M. J. (2023). Youtube ”adpocalypse”: The youtubers’ journey from ad-based to patron-based revenues. ZEW-Centre for European Economic Research Discussion Paper, (59).
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.
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.
Publicado
02/06/2025
Como Citar
ALMEIDA, Luis Gustavo; GARCIA, Ana Cristina B.; SIMÕES, Jefferson Elbert.
Polarização no YouTube: Interação Colaborativa e a Atração dos Discursos Fortes e Monetização - Proposta de uma Ferramenta Online. In: SIMPÓSIO BRASILEIRO DE SISTEMAS COLABORATIVOS (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.
