Influência da Recomendação Algorítmica no Bem-Estar dos Usuários: Proposta de Auditoria
Resumo
A influência de algoritmos de recomendação sobre o bem-estar de usuários em estado de vulnerabilidade psicológica levanta preocupações sobre a responsabilidade ética das plataformas digitais. Para explorar este contexto, este estudo propõe uma auditoria algorítmica para analisar se esses sistemas criam loops de retroalimentação que saturam o feed com conteúdos sensíveis no contexto de distorção de imagem. Espera-se que os resultados possam inspirar diretrizes de transparência algorítmica que privilegiem o bem-estar digital em sistemas colaborativos.
Palavras-chave:
algoritmos de recomendação, auditoria algorítmica, vulnerabilidade psicológica
Referências
Bandy, J. (2021). Problematic machine behavior: A systematic literature review of algorithm audits. Proceedings of the acm on human-computer interaction, 5(CSCW1):1–34.
Bardin, L. (1977). Análise de conteúdo. Edições 70.
Boeker, M. and Urman, A. (2022). An empirical investigation of personalization factors on tiktok. In Proceedings of the ACM Web Conference 2022 (WWW ’22). Association for Computing Machinery.
Creswell, J. W. and Clark, V. P. (2007). Mixed methods research. Thousand Oaks, CA.
Golbeck, J. A. (2025). Recommender system-induced eating disorder relapse: Harmful content and the challenges of responsible recommendation. ACM Transactions on Intelligent Systems and Technology, 16(1).
Lin, L. Y., Sidani, J. E., Shensa, A., Radovic, A., Miller, E., Colditz, J. B., Hoffman, B. L., Giles, L. M., and Primack, B. A. (2016). Association between social media use and depression among us young adults. Depression and anxiety, 33(4):323–331.
McCrory, A., Best, P., and Maddock, A. (2022). ‘it’s just one big vicious circle’: young people’s experiences of highly visual social media and their mental health. Health Education Research.
Mosnar, M., Skurla, A., Pecher, B., Tibensky, M., Jakubcik, J., and Bindas, A. (2025). Revisiting algorithmic audits of tiktok: Poor reproducibility and short-term validity of findings. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’25). Association for Computing Machinery.
Pendse, S. R., Kumar, N., and De Choudhury, M. (2023). Marginalization and the construction of mental illness narratives online: Foregrounding institutions in technology-mediated care. In Proceedings of the ACM on Human-Computer Interaction.
Sandvig, C., Hamilton, K., Karahalios, K., and Langbort, C. (2014). Auditing algorithms: Research methods for detecting discrimination on internet platforms. In ICA Data and Discrimination Preconference, Seattle, WA, USA.
Shen, H., DeVos, A., Eslami, M., and Holstein, K. (2021). Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2):1–29.
Vombatkere, K., Mousavi, S., Zannettou, S., Roesner, F., and Gummadi, K. P. (2024). Tiktok and the art of personalization: Investigating exploration and exploitation on social media feeds. In Proceedings of the ACM Web Conference 2024 (WWW ’24), pages 3789–3797. Association for Computing Machinery.
Bardin, L. (1977). Análise de conteúdo. Edições 70.
Boeker, M. and Urman, A. (2022). An empirical investigation of personalization factors on tiktok. In Proceedings of the ACM Web Conference 2022 (WWW ’22). Association for Computing Machinery.
Creswell, J. W. and Clark, V. P. (2007). Mixed methods research. Thousand Oaks, CA.
Golbeck, J. A. (2025). Recommender system-induced eating disorder relapse: Harmful content and the challenges of responsible recommendation. ACM Transactions on Intelligent Systems and Technology, 16(1).
Lin, L. Y., Sidani, J. E., Shensa, A., Radovic, A., Miller, E., Colditz, J. B., Hoffman, B. L., Giles, L. M., and Primack, B. A. (2016). Association between social media use and depression among us young adults. Depression and anxiety, 33(4):323–331.
McCrory, A., Best, P., and Maddock, A. (2022). ‘it’s just one big vicious circle’: young people’s experiences of highly visual social media and their mental health. Health Education Research.
Mosnar, M., Skurla, A., Pecher, B., Tibensky, M., Jakubcik, J., and Bindas, A. (2025). Revisiting algorithmic audits of tiktok: Poor reproducibility and short-term validity of findings. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’25). Association for Computing Machinery.
Pendse, S. R., Kumar, N., and De Choudhury, M. (2023). Marginalization and the construction of mental illness narratives online: Foregrounding institutions in technology-mediated care. In Proceedings of the ACM on Human-Computer Interaction.
Sandvig, C., Hamilton, K., Karahalios, K., and Langbort, C. (2014). Auditing algorithms: Research methods for detecting discrimination on internet platforms. In ICA Data and Discrimination Preconference, Seattle, WA, USA.
Shen, H., DeVos, A., Eslami, M., and Holstein, K. (2021). Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2):1–29.
Vombatkere, K., Mousavi, S., Zannettou, S., Roesner, F., and Gummadi, K. P. (2024). Tiktok and the art of personalization: Investigating exploration and exploitation on social media feeds. In Proceedings of the ACM Web Conference 2024 (WWW ’24), pages 3789–3797. Association for Computing Machinery.
Publicado
08/06/2026
Como Citar
LUZ, Ana Beatriz F. da; FIGUEIREDO, Mayara C..
Influência da Recomendação Algorítmica no Bem-Estar dos Usuários: Proposta de Auditoria. In: DESENHO DE PESQUISA - SIMPÓSIO BRASILEIRO DE SISTEMAS COLABORATIVOS (SBSC), 21. , 2026, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 39-44.
DOI: https://doi.org/10.5753/sbsc_estendido.2026.20352.
