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