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

 Javier García-Marín, Granada (Spain)

 Ignacio-Jesús Serrano-Contreras, Jaén (Spain)

https://doi.org/10.3916/C74-2023-05

Keywords

Machine learning, YouTube, social media, recommendation system, polarisation, communication

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.

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Fundref

This research is part of the R&D&I project PID2021-128272NB-I00 funded by the Spanish Ministry of Science and Innovation MCIN/ AEI/10.13039/501100011033/ ERDF ”A way of doing Europe”.

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Technical information

Received: 30-05-2022

Revised: 21-06-2022

Accepted: 13-07-2022

OnlineFirst: 30-10-2022

Publication date: 01-01-2023

Article revision time: 22 days | Average time revision issue 74: 40 days

Article acceptance time: 44 days | Average time of acceptance issue 74: 69 days

Preprint editing time: 171 days | Average editing time preprint issue 74: 194 days

Article editing time: 216 days | Average editing time issue 74: 239 days

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