(Un)founded fear towards the algorithm: YouTube recommendations and polarisation

García-Marín, Javier and Serrano-Contreras, Ignacio-Jesús (Un)founded fear towards the algorithm: YouTube recommendations and polarisation. Comunicar, 2023, vol. 31, n. 74, pp. 61-70. [Journal article (Paginated)]

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English abstract

Social media have established a new way of communicating and understanding social relationships. At the same time, there are downsides, especially, their use of algorithms that have been built and developed under their umbrella and their potential to alter public opinion. This paper tries to analyse the YouTube recommendation system from the perspectives of reverse engineering and semantic mining. The first result is that, contrary to expectations, the issues do not tend to be extreme from the point of view of polarisation in all cases. Next, and through the study of the selected themes, the results do not offer a clear answer to the proposed hypotheses, since, as has been shown in similar works, the factors that shape the recommendation system are very diverse. In fact, results show that polarising content does not behave in the same way for all the topics analysed, which may indicate the existence of moderators –or corporate actions– that alter the relationship between the variables. Another contribution is the confirmation that we are dealing with non-linear, but potentially systematic, processes. Nevertheless, the present work opens the door to further academic research on the topic to clarify the unknowns about the role of these algorithms in our societies.

Spanish abstract

Las redes sociales han instaurado una nueva forma de comunicarse y entender las relaciones sociales. A su vez, en lo que podría entenderse como un aspecto negativo, los algoritmos se han construido y desarrollado bajo el paraguas de un amplio abanico de conjeturas y diferentes posiciones al respecto de su capacidad para dirigir y orquestar la opinión pública. El presente trabajo aborda, desde los procesos de ingeniería inversa y de minado semántico, el análisis del sistema de recomendación de YouTube. De este modo, y, en primer lugar, reseñar un resultado clave, las temáticas analizadas de partida no tienden a extremarse. Seguidamente, y mediante el estudio de los temas seleccionados, los resultados no ofrecen una clara resolución de las hipótesis propuestas, ya que, como se ha mostrado en trabajos parecidos, los factores que dan forma al sistema de recomendación son variados y de muy diversa índole. De hecho, los resultados muestran cómo el contenido polarizante no es igual para todos los temas analizados, lo que puede indicar la existencia de moderadores –o acciones por parte de la compañía– que alteran la relación entre las variables. Con todo ello, trabajos como el presente abren la puerta a posteriores incursiones académicas en las que trazar sistematizaciones no lineales y con las que, tal vez, poder arrojar un sustento más neto y sustancial que permita despejar por completo parte de las dudas sobre el papel de los algoritmos y su papel en fenómenos sociales recientes.

Item type: Journal article (Paginated)
Keywords: Machine learning; YouTube; social media; recommendation system; polarisation; communication; Aprendizaje de máquina; YouTube; redes sociales; sistemas de recomendación; polarización; comunicación
Subjects: B. Information use and sociology of information > BJ. Communication
G. Industry, profession and education.
G. Industry, profession and education. > GH. Education.
Depositing user: Alex Ruiz
Date deposited: 09 Jan 2023 06:32
Last modified: 09 Jan 2023 06:32
URI: http://hdl.handle.net/10760/43880

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