Volume index - Journal index - Article index - Map ---- Back

Comunicar Journal 74: Education for digital citizenship: Algorithms, automation and communication (Vol. 31 - 2023)

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


Javier García-Marín

Ignacio-Jesús Serrano-Contreras


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.


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

PDF file in Spanish

PDF file in English


Alfano, M., Fard, A.E., Carter, J.A., Clutton, P., & Klein, C. (2021). Technologically scaffolded atypical cognition: The case of YouTube’s recommender system. Synthese, 199(1-2), 835-858. https://doi.org/10.1007/s11229-020-02724-x

Almagro, M., & Villanueva, N. (2021). Polarización y tecnologías de la Información: Radicales vs. extremistas. Dilemata, 34, 51-69. https://bit.ly/38YwIiH

Arceneaux, K., & Johnson, M. (2010). Does media fragmentation produce mass polarization? Selective exposure and a new era of minimal effects. In A. Campbell, & L. Martin (Eds.), American Political Science Association 2010 Annual Meeting. SSRN. https://bit.ly/3M1e7jJ

Arias-Maldonado, M. (2016). La digitalización de la conversación pública: Redes sociales, afectividad política y democracia. Revista de Estudios Políticos, 173, 27-54. https://doi.org/10.18042/cepc/rep.173.01

Bail, C.A. (2021). Breaking the social media prism: How to make our platforms less polarizing. Princeton University Press. https://doi.org/10.1515/9780691216508

Banaji, S. (2013). Everyday racism and «My tram experience»: Emotion, civic performance and learning on YouTube. [El racismo cotidiano y «Mi experiencia en un tranvía»: emoción, comportamiento cívico y aprendizaje en YouTube]. Comunicar, 40, 69-78. https://doi.org/10.3916/C40-2013-02-07

Barberá, P. (2020). Social media, echo chambers, and political polarization. In N. Persily, & J. Tucker (Eds.), Social media and democracy: The state of the field, prospects for reform (pp. 34-55). Cambridge University Press. https://doi.org/10.1017/9781108890960

Berners-Lee, T. (2000). Tejiendo la red. Siglo XXI de España. https://bit.ly/3wZ1NMx

Berrocal-Gonzalo, S., Campos-Domínguez, E., & Redondo-García, M. (2014). Media prosumers in political communication: Politainment on YouTube. [Prosumidores mediáticos en la comunicación política: El «politainment» en YouTube]. Comunicar, 43, 65-72. https://doi.org/10.3916/C43-2014-06

Bishop, S. (2018). Anxiety, panic and self-optimization: Inequalities and the YouTube algorithm. Convergence, 24(1), 69-84. https://doi.org/10.1177/1354856517736978

Castells, M. (2001). La era de la información: Economía, sociedad y cultura. Alianza Editorial. https://bit.ly/3LXI18w

Chadwick, A. (2009). Web 2.0: New challenges for the study of e-democracy in an era of informational exuberance. I/S: A Journal of Law and Policy for the Information Society, 5(1), 9-41. https://bit.ly/3MZopSH

Chen, A., Nyhan, B., Reifler, J., Robertson, R., & Wilson, C. (2021). Exposure to alternative & extremist content on YouTube. Anti-Defamation League. https://bit.ly/3MZ19E9

Covington, P., Adams, J., & Sargin, E. (2016). Deep neural networks for YouTube recommendations. In S. Sen, & W. Geyer (Eds.), Proceedings of the 10th ACM Conference on Recommender Systems, (pp. 191-198). Association for Computing Machinery. https://doi.org/10.1145/2959100.2959190

Davidson, J., Livingston, B., Sampath, D., Liebald, B., Liu, J., Nandy, P., Van-Vleet, T., Gargi, U., Gupta, S., He, Y., & Lambert, M. (2010). The YouTube video recommendation system. In X. Amatriain, M. Torrens, P. Resnick, & M. Zanker (Eds.), Proceedings of the fourth ACM conference on Recommender Systems, (pp. 293-296). Association for Computing Machinery. https://doi.org/10.1145/1864708.1864770

Demsar, J., Curk, T., Erjavec, A., Gorup, C., Hocevar, T., Milutinovic, M., Mozina, M., Polajnar, M., Toplak, M., Staric, A., Stajdohar, M., Umek, L., Zagar, L., Zbontar, J., Zitnik, M., & Zupan, B. (2013). Orange: Data mining toolbox. Python. The Journal of Machine Learning Research, 14(1), 2349-2353. https://bit.ly/3pMIPBR

Dimopoulos, G., Barlet-Ros, P., & Sanjuas-Cuxart, J. (2013). Analysis of YouTube user experience from passive measurements. In Proceedings of the 9th International Conference on Network and Service Management (CNSM 2013), (pp. 260-267). IEEE. https://doi.org/10.1109/CNSM.2013.6727845

Goodrow, C. (2021). On YouTube’s recommendation system. Blog YouTube. https://bit.ly/3wWAxhA

Habermas, J. (1981). Historia y crítica de la opinión pública. Gustavo Gili. https://bit.ly/3O0JOv1

Hernández, E., Anduiza, E., & Rico, G. (2021). Affective polarization and the salience of elections. Electoral Studies, 69, 102203. https://doi.org/10.1016/j.electstud.2020.102203

Howard, J.W. (2021). Extreme speech, democratic deliberation, and social media. In C. Véliz (Ed.), The Oxford Handbook of Digital Ethics (pp. 1-22). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780198857815.013.10

Iyengar, S., Lelkes, Y., Levendusky, M., Malhotra, N., & Westwood, S.J. (2019). The origins and consequences of affective polarization in the United States. Annual Review of Political Science, 22, 129-146. https://doi.org/10.1146/annurev-polisci-051117-073034

Latorre, M (2022). Historia de la Web, 1.0, 2.0, 3.0 y 4.0. Blog Marino Latorre. https://bit.ly/38un7QH

Lilleker, D.G., & Jackson, N. (2008). Politicians and Web 2.0: The current bandwagon or changing the mindset? [Conference]. Politics: Web 2.0 International Conference.

Luengo, O., García-Marín, J., & de-Blasio, E. (2021). COVID-19 on YouTube: Debates and polarisation in the digital sphere. [COVID-19 en YouTube: Debates y polarización en la esfera digital]. Comunicar, 69, 9-19. https://doi.org/10.3916/C69-2021-01

McLuhan, H.M. (1959). Myth and mass media. Daedalus, 88(2), 339-348. https://bit.ly/3GtIs9v

Messina, J.P. (2022). New directions in the ethics and politics of speech. Routledge. https://doi.org/10.4324/9781003240785

Mohan, N. (2022). Inside responsibility: What’s next on our misinfo efforts. Blog YouTube. https://bit.ly/38XAngS

Nielsen, R., & Fletcher, R. (2020). Democratic creative destruction? The Effect of a changing media landscape on democracy. In N. Persily, & J. Tucker (Eds.), Social media and democracy: The state of the field, prospects for reform (pp. 139-162). Cambridge University Press. https://doi.org/10.1017/9781108890960.008

O'Reilly, T., & Battelle, J. (2009). Web squared: Web 2.0 five years on. O'Reilly Media. https://bit.ly/3wYLBuG

Pariser, E. (2017). El filtro burbuja: Cómo la web decide lo que leemos y lo que pensamos. Taurus. https://bit.ly/3x0UyDX

Rasmussen, S.H.R., & Petersen, M. (2022). From echo chambers to resonance chambers: How offline political events enter and are amplified in online networks. PsyArXiv. https://doi.org/10.31234/osf.io/vzu4q

Rekoff, M.G. (1985). On reverse engineering. IEEE Transactions on Systems, Man, and Cybernetics, 15(2), 244-252. https://doi.org/10.1109/TSMC.1985.6313354

Serrano-Contreras, I., García-Marín, J., & Luengo, O. G. (2020). Measuring online political dialogue: Does polarization trigger more deliberation? Media and Communication, 8(4), 63-72. https://doi.org/10.17645/mac.v8i4.3149

Sunstein, C.R. (2007). Republic.com 2.0. Princeton University Press. https://bit.ly/3a3YFG8

Terren, L., & Borge-Bravo, R. (2021). Echo chambers on social media: A systematic review of the literature. Review of Communication Research, 9, 99-118. https://doi.org/10.12840/ISSN.2255-4165.028

Tufekci, Z. (2018, March 20). YouTube, the great radicalizer. The New York Times. https://nyti.ms/38VTs2Y

Van-Bavel, J.J., Rathje, S., Harris, E., Robertson, C., & Sternisko, A. (2021). How social media shapes polarization. Trends in Cognitive Sciences, 25(11), 913-916. https://doi.org/10.1016/j.tics.2021.07.013

Wigand, R., Wood, J., & Mande, D. (2010). Taming the social network jungle: From Web 2.0 to social media [Conference]. AMCIS 2010 Proceedings. https://bit.ly/3NJF3Wl

Yesilada, M., & Lewandowsky, S. (2022). Systematic review: YouTube recommendations and problematic content. Internet Policy Review, 11(1). https://doi.org/10.31234/osf.io/6pv5c