ALRECO (2020). Foros de Debate acerca del discurso de odio en las redes. ALRECO Website Consultado 2021, julio en 

Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE access, 6, 52138-52160. 

Alonso García, Alonso y otros, “The Impact of Term Fake News on the Scientific Community. Scientific Performance and Mapping in Web of Science”, Social Science 9, 73 (2020). doi:10.3390/socsci9050073 

Angwin, J.; Larson, J.; Mattu, S.; and Kirchner, L. 2016. Machine bias. ProPublica .

Badjatiya, P.; Gupta, S.; Gupta, M,; Varma, V. (2017) Deep Learning for Hate Speech Detection in Tweets WWW ’17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion.April 2017 Pages 759–760 

Barocas, Solon, Moritz Hardt, and Arvind Narayanan. «Fairness in machine learning.» Nips tutorial 1 (2017): 2017. 

Bessi, A. and Ferrara, E. Social bots distort the 2016 US presidential election online discussion. First Monday, 21(11), 2016. 

Bolukbasi, Tolga, et al. «Man is to computer programmer as woman is to homemaker? debiasing word embeddings.» Advances in neural information processing systems 29 (2016): 4349-4357. 

Buolamwini, Joy, and Timnit Gebru. «Gender shades: Intersectional accuracy disparities in commercial gender classification.» Conference on fairness, accountability and transparency. PMLR, 2018. 

Bury, C. (2017). Positive online content: better experiences for children. European Commission. 

Caliskan, Aylin, Joanna J. Bryson, and Arvind Narayanan. «Semantics derived automatically from language corpora contain human-like biases.» Science 356.6334 (2017): 183-186. 

Chakravarthi, B. R. (2020). HopeEDI: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion. Proceedings of the Third Workshop on Computational Modeling of PEople’s Opinions, PersonaLity, and Emotions in Social media, pages 41–53 Barcelona, Spain (Online), December 13, 2020. 

Chernyavskiy, A., Ilvovsky, D., & Nakov, P. (2021). Transformers:» The End of History» for NLP?. arXiv preprint arXiv:2105.00813. 

Corazza, M.;Menini, S. Cabrio, E.; Tonelli, S. and Villata, S. (2020). A Multilingual Evaluation for Online Hate Speech Detection. ACM Trans. Internet Technol. 20, 2, Article 10, 22 pages. 

Danilevsky, M., Qian, K., Aharonov, R., Katsis, Y., Kawas, B., & Sen, P. (2020, December). A Survey of the State of Explainable AI for Natural Language Processing. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing (pp. 447-459). 

Du, M., Liu, N., & Hu, X. (2019). Techniques for interpretable machine learning. Communications of the ACM, 63(1), 68-77. 

European Commission (2019). Progress on combating hate speech online through the EU Code of conduct 2016-2019. Recuperado el 16/09/2021 

European Commission (2021). First baseline reports – Fighting COVID-19 disinformation Monitoring Programme. Shaping Europe’s digital future. Consultado el 30/6/2021 en 

Farmer, F. & Tremlett, K. (2020) (2020). Through These Walls Impartial dispute resolution of online harm during a global pandemic. Report Harmful Content. Online Safety, Security, and Education Technology. Consultado el 30/6/2021 en 

Gaggioli, A.; Riva, G.; Peters, D.; Calvo, R.A.(2017). Positive Technology, Computing, and Design: Shaping a Future in Which Technology Promotes Psychological Well-Being, Ed): Myounghoon Jeon, Emotions and Affect in Human Factors and Human-Computer Interaction, Academic Press, 

Garrido-Muñoz, I., Montejo-Ráez, A., Martínez-Santiago, F., & Ureña-López, L. A. (2021). A Survey on Bias in Deep NLP. Applied Sciences, 11(7), 3184. 

GFK (2020, noviembre). Las tendencias en consumo digital que han llegado para quedarse. GFK Consultado el 24/07/2021 en 

Golovin, D., Solnik, B., Moitra, S., Kochanski, G., Karro, J., & Sculley, D. (2017, August). Google vizier: A service for black-box optimization. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1487-1495). 

Hacker, P. (2018). Teaching fairness to artificial intelligence: Existing and novel strategies against algorithmic discrimination under EU law. Common Market Law Review, 55(4). 

Hamming, R. W. (April 1950). «Error detecting and error correcting codes» (PDF). The Bell System Technical Journal. 29 (2): 147–160. doi:10.1002/j.1538-7305.1950.tb00463.x. ISSN 0005-8580. 

Interactive Advertising Bureau [IAB] Spain (2016). Estudio anual de redes sociales.

Interactive Advertising Bureau [IAB] Spain (2021). Estudio de Redes Sociales 2021. 

Keipi, T., Nasi, M., Oksanen, A. & Rasanen, P. (2017) Online hate and harmful content: cross-national perspectives, First;1; edn, Routledge, New York. 

Liu, H., & Motoda, H. (Eds.). (2007). Computational methods of feature selection. CRC Press. 

Lloret-Climent, M., Nescolarde-Selva, J. A., Selva Barthelemy, M., & Alonso-Stenberg, K. (2018). “Smarta v1.0”. Universidad de Alicante. 

López, J. et al. (2020). Informe sobre la evolución de los delitos de odio en España. Oficina Nacional de Lucha contra los Delitos de Odio, Ministerio del Interior, Gobierno de España. 2020. de+delitos+de+odio+en+Espa%C3%B1a+a%C3%B1o+2020.pdf/bc4738d2-ebe6-434f-9516- 5d511a894cb9 

Mathew, B.; Illendula, A.; Saha, P. Sarkar, S.; Goyal, P.; and Mukherjee, A. (2020). Hate begets Hate: A Temporal Study of Hate Speech. Proc. ACM Hum.-Comput. Interact. 4, CSCW2, Article 92, 24 pages. 

Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.). (2013). Machine learning: An artificial intelligence approach. Springer Science & Business Media. 

Minsky, M. (1975) «A Framework for Representing Knowledge», en P. Winston (ed.) The Psychology of Computer Vision. New York: McGraw-Hill. 211-277. 

Monteiro Borges, P. y Rampazzo Gambarato, R. (2019). The Role of Beliefs and Behavior on Facebook: A Semiotic Approach to Algorithms, Fake News, and Transmedia Journalism. International Journal of Communication, 13, pp. 603-618. 

Muñiz-Velazquez, J.A. y Pulido, C.M. (2019). The Routledge Handbook of Positive Communication. Contributions of an Emerging Community of Research on Communication for Happiness and Social Change. Eds. Muñiz-Velazquez y Pulido. Londres: Routledge. 

Pires, Telmo; Schlinger, Eva and Garrette, Dan (2019). How Multilingual is Multilingual BERT?. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4996–5001. doi:10.18653/v1/P19-1493 

Powers, Shawn; Kounalakis, Markos (eds.) (2017) Can public democracy survive the Internet? Bots, echo chambers, and disinformation. Washington: U.S. Advisory Commission on Public Diplomacy (Department of State).

Radford, A et al. (2021). Learning Transferable Visual Models From Natural Language Supervision. arXiv preprint arXiv:2103.00020 

Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 1, 206–215 (2019). 019-0048-x 

Shannon, Claude Elwood; Weaver, Warren (1949). A Mathematical Theory of Communication (PDF). University of Illinois Press. ISBN 0-252-72548-4. Archived from the original (PDF) on 1998-07- 15.

Siwakoti, S., Yadav, K., Bariletto, N., Zanotti, L., Erdoğdu, U., & Shapiro, J. N. (2021). How COVID drove the evolution of fact-checking. Harvard Kennedy School (HKS) Misinformation Review, 2(3). 

Social Blade (julio; 2021). Social Blade. 

Statista (9/2020). A Minute on the Internet in 2020. Statista. Retrieved from 

Wardle, Claire; Derakhshan, Hossein (2017). Information Disorder: Toward an interdisciplinary framework for research and policy making. Council of Europe Report DGI(2017)09. Bruselas: Consejo de Europa. 

Wardle, C.; Derakhshan, H. (2018). “Thinking about ‘information disorder’: formats of misinformation, disinformation, and mal-information”. In Cherilyn Ireton and Julie Posetti (eds.), Journalism, ‘fake news’ & disinformation: A handbook for journalism education and training. Paris: UNESCO, pp. 44-56. 

Xia, C., Zhang, C., Zhang, J., Liang, T., Peng, H., & Philip, S. Y. (2020, October). Low-shot Learning in Natural Language Processing. In 2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) (pp. 185-189). IEEE