Curadoria algorítmica nas plataformas de streaming e os efeitos adversos dos sistemas de recomendação
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Universidade Federal de São Carlos
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This research, within the scope of studies in Science, Technology and Society, has as its theme
the relationship between the cultural industry, artificial intelligence and cultural diversity, in
which it studies if, and how, the recommendation systems of the streaming platforms
Globoplay and Netflix act towards the creation of social bubbles due to the personalization,
made through the consumption of audiovisual materials. It is justified in the sense of
exploring the effects caused by intelligent algorithms, whether intentional or not, which go
unnoticed by the public and can influence their ways of thinking and seeing the world. The
objective, therefore, is to understand the performance of recommendation systems on
audiovisual streaming platforms. The hypothesis raised is that the intelligent algorithms of
recommendation systems contribute to the formation of social bubbles, in order to reaffirm
users' social, cultural and political convictions. The research is divided into four fundamental
stages: the description of the research objects, the survey of Latin American titles present on
the platforms, the analysis of the home screen of these platforms and the discussion about
algorithms and society. The characteristic of a recommendation system, based on AI and big
data, of learning from the user and indicating content that matches their profile, gives
evidence that the hypothesis may be true, as the user's profile, films and series that relate to
this profile can be more recommended to him. However, the opacity of streaming platforms
does not allow us to reach an affirmative conclusion. One solution suggested for the problems
raised here focuses on transparency, allowing users to access their profile records and adjust
what is recommended, and also on the regulation of platforms by the State.
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LIMA, Lucas Peixoto de. Curadoria algorítmica nas plataformas de streaming e os efeitos adversos dos sistemas de recomendação. 2023. Tese (Doutorado em Ciência, Tecnologia e Sociedade) – Universidade Federal de São Carlos, São Carlos, 2023. Disponível em: https://repositorio.ufscar.br/handle/20.500.14289/19006.
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