Algorithmic mediatization: "the programming effect"
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In sound and audiovisual streaming platforms –such as Spotify or YouTube– there are some functions based on machine learning Artificial Intelligence systems that generate a personalized selection of items that are played one after the other with no need of user interaction. In these cases, a flow of the audiovisual image (or musical audio, or podcasts) unfolds. This flow evokes, in some way, the phenomenon of mass media programming: in a way similar to the one we met in radio or television, a flow of content appears again in which the decision of what will be seen and in what order is on the broadcast/production side. Although this programming has several differences with that of the mass media, in these operations there seems to be a backwards movement: if the platforms originally appeared as the option for each person to decide what they want to consume, in these cases, the platforms again offer us to choose for ourselves, as if the programming of consumption were once again delegated to production, just as it happened in the heyday of the mass media.
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(c) Mariano Zelcer, 2023
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).
Mariano Zelcer, Universidad Nacional de las Artes, Argentina
Mariano Zelcer. He holds a PhD in Communication (UNLP) and a Bachelor of Communication (UBA). He is Associate Professor of Semiotics and Communication Theory at the National University of the Arts, Argentina, where he teaches undergraduate and postgraduate courses. In his different papers, he has written about various phenomena related to the Internet and digital communication, among them, the machine learning algorithms that govern different content selection and recommendation systems (music, videos, advertising), one of the expansion spaces of Artificial Intelligence today. He has published articles in numerous academic journals, as well as book chapters. In 2021 he published Devenires de lo fotógráfico (Editorial Teseo, Buenos Aires).
Carlón, M. (2016). Después del fin. Una perspectiva no antropocéntrica sobre la post-tv, el post-cine y youtube. La Crujía.
Ceci, L. (Septiembre de 2021). Distribution of worldwide YouTube viewing time as of 2nd quarter 2021, by device. Statista. https://www.statista.com/statistics/1173543/youtube-viewing-time-share-device/
Cingolani, G. (2017a). Sistemas de recomendación, mediatizaciones de lo preferible y enunciación. En M. P. Busso y M. Camuso (Eds.), Mediatizaciones en tensión: el atravesamiento de lo público, (pp. 30-47). UNR Editora.
Cingolani, G. (2017b). Estrategias para el acceso: los sitios de recomendación como espacios de tensiones en la circulación y mediatización del reconocimiento. En P. C. Castro (Org.), A circulação discursiva: entre produção e reconhecimento, (pp. 125-140). EDUFAL.
Ciocca, S. (10 de octubre de 2017). How Does Spotify Know You So Well? Medium. https://medium.com/s/story/spotifys-discover-weekly-how-machine-learning-finds-your-new-music-19a41ab76efe
Fernández, J. L. (2016). Plataformas mediáticas y niveles de análisis. Inmediaciones de la comunicación número 11, 71-96.
Fernández, J. L. (2022). Vidas mediáticas. Entre lo masivo y lo individual. La Crujía.
Freeman, S. (2017). Because you liked… A study of automated music discovery and algorithmic culture. Academia.edu. https://www.academia.edu/34494608/Because_You_Liked
Manovich, L. (2012). El software toma el mando. UOC Press
Pérez-Rufi, J.P. (2011). YouTube ya no es “tu televisión”: cultura colaborativa y red comercial en el vídeo online. Revista Comunicación, 1(9), 146-162.
Ricci, F., Rokach, L. y Shapira, B. (2015). Recommender Systems Handbook. Springer.
Solsman, J. E. (10 de enero de 2018). YouTube’s AI is the puppet master over most of what you watch. CNET. https://www.cnet.com/tech/services-and-software/youtube-ces-2018-neal-mohan
Traversa, O. (2001). Aproximaciones a la noción de dispositivo. Signo y Seña, 12, 233- 247. http://revistascientificas.filo.uba.ar/index.php/sys/article/view/5612
Traversa, O. (2009). Dispositivo-enunciación: en torno a sus modos de articularse. Figuraciones, 6. https://repositorio.una.edu.ar/handle/56777/1086
Van Dijk, J. (2016). La cultura de la conectividad. Una historia crítica de las redes sociales, Siglo XXI.
Verón, E. (2009). El fin de la historia de un mueble. En M. Carlón y C. Scolari (Eds.), El fin de los medios masivos. El comienzo de un debate, (pp. 229-250). La Crujía.
Videla, S. (2019). Plataformas de streaming audiovisual. La construcción del usuario. Academia.edu. https://www.academia.edu/42688763/PLATAFORMAS_DE_STREAMING_AUDIOVISUAL_LA_CONSTRUCCI%C3%93N_DEL_USUARIO
Zelcer, M. (2021). Devenires de lo fotográfico. Imágenes digitales en los dispositivos contemporáneos. Teseo. https://www.teseopress.com/devenires
Zelcer, M. (2023a). Sistemas de recomendación en plataformas de streaming audiovisual: las lógicas de los algoritmos. Revista Mídia & Cotidiano 17(2). https://periodicos.uff.br/midiaecotidiano/article/view/57130
Zelcer, M. (2023b). De las audiencias mediáticas a las algorítmicas. Pensar la publicidad, 16 (en prensa). https://revistas.ucm.es/index.php/pepu
Zelcer, M., Cingolani, G. y Koldobsky, D. (2023). Sistemas de recomendación y curaduría automatizada. Inédito.
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