Keywords

Redes sociales, cyberbullying, educación superior, estudiantes universitarios, tic, inteligencia artificial

Abstract

El acoso escolar implica comportamientos repetitivos y agresivos para intimidar o dañar a otros, siendo el ciberacoso el tipo que se beneficia de las plataformas digitales para transmitir tales mensajes. Ambos tienen consecuencias graves sobre la salud mental y el rendimiento académico de los estudiantes, lo que requiere estrategias de prevención e intervención, incluyendo la capacitación de los docentes y el uso de herramientas para detectar estos comportamientos en los entornos universitarios. La inteligencia artificial (IA), específicamente la IA generativa, puede detectar automáticamente el lenguaje ofensivo en las plataformas digitales, convirtiéndose en una herramienta eficaz para combatir el ciberacoso. Este estudio analiza la capacidad de los miembros de la comunidad universitaria—estudiantes, docentes y personal administrativo—para percibir y detectar el ciberacoso en los mensajes de redes sociales. La investigación utiliza herramientas de IA generativa para evaluar su efectividad en reconocer patrones de ciberacoso, comparando los resultados con las evaluaciones de expertos. Los resultados indican que los docentes son los más efectivos en identificar el ciberacoso, mientras que los estudiantes muestran mayor indulgencia, lo que resalta la necesidad de intervenciones educativas más específicas. A pesar de sus limitaciones, los modelos de IA generativa demuestran un gran potencial para la detección temprana del ciberacoso. Los hallazgos subrayan la importancia de la formación dentro de las comunidades educativas y sugieren que las herramientas de IA, cuando se integran en programas preventivos, pueden mejorar la intervención temprana y promover entornos.

References

Abarna, S., Sheeba, J. I., Jayasrilakshmi, S. y Devaneyan, S. P. (2022). Identification of cyber harassment and intention of target users on social media platforms. Engineering Applications of Artificial Intelligence, 115, 105283. https://doi.org/10.1016/j.engappai.2022.105283
Aggarwal, A. y Gaur, S. (2024). Applying Artificial Intelligence to Explore Online Harassment and Cyberbullying Prevention. En S. Ponnusamy, V. Bora, P. M. Daigavane, y S. S. Wazalwar (Eds.), Impact of AI on Advancing Women’s Safety (pp. 104-120). IGI Global. https://doi.org/10.4018/979-8-3693-2679-4.ch007
Azeez, N. A., Idiakose, S. O., Onyema, C. J. y Van Der Vyver, C. (2021). Cyberbullying Detection in Social Networks: Artificial Intelligence Approach. Journal of Cyber Security and Mobility, 10(4), 745-774. https://doi.org/10.13052/jcsm2245-1439.1046
Baidoo-Anu, D. y Owusu Ansah, L. (2023). Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning. Journal of AI, 7(1), 52-62. https://doi.org/10.61969/jai.1337500
Baldassarre, M. T., Caivano, D., Fernandez Nieto, B., Gigante, D. y Ragone, A. (2023). The Social Impact of Generative AI: An Analysis on ChatGPT. En Proceedings of the 2023 ACM Conference on Information Technology for Social Good (pp. 363 373). Association for Computing Machinery. https://doi.org/10.1145/3582515.3609555
Bandi, A., Adapa, P. V. S. R. y Kuchi, Y. E. V. P. K. (2023). The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats, Evaluation Metrics, and Challenges. Future Internet, 15(8), 260. https://doi.org/10.3390/fi15080260
Barlett, C. P. (2015). Anonymously Hurting Others Online: The Effect of Anonymity on Cyberbullying Frequency. Psychology of Popular Media Culture, 4(2), 70-79. https://doi.org/10.1037/a0034335
Barreto, F., Moharkar, L., Shirodkar, M., Sarode, V., Gonsalves, S. y Johns, A. (2023). Generative Artificial Intelligence: Opportunities and Challenges of Large Language Models. En V. E. Balas, V. B. Semwal, y A. Khandare (Eds.), Intelligent Computing and Networking (pp. 545-553). Springer Nature Singapore. https://doi.org/10.1007/978-981-99-3177-4_41
Burger, C., Strohmeier, D. y Kollerová, L. (2022). Teachers Can Make a Difference in Bullying: Effects of Teacher Interventions on Students’ Adoption of Bully, Victim, Bully-Victim or Defender Roles across Time. Journal of Youth and Adolescence, 51(12), 2312-2327. https://doi.org/10.1007/s10964-022-01674-6
Campbell, M., Whiteford, C. y Hooijer, J. (2019). Teachers’ and parents’ understanding of traditional and cyberbullying. Journal of School Violence, 18(3), 388-402. https://doi.org/10.1080/15388220.2018.1507826
Cerezo, F. (2009). Bullying: análisis de la situación en las aulas españolas. International Journal of Psychology and Psychological Therapy, 9(3), 383-394. https://bit.ly/42UCz0c
Chai, T. y Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247-1250. https://doi.org/10.5194/gmd-7-1247-2014
Chaudhary, S., Yadav, S., Kushwaha, S. y Shahi, S. R. P. (2020). A Brief Review of Machine Learning and its Applications. Samriddhi: A Journal of Physical Sciences, Engineering and Technology, 12(SUP 1), 218-223. https://www.smsjournals.com/index.php/SAMRIDDHI/article/view/1941
Cortés, A. F. M., De los Río, O. L. H. y Pérez, A. S. (2019). Factores de riesgo y factores protectores relacionados con el ciberbullying entre adolescentes: una revisión sistemática. Papeles del psicólogo, 40(2), 109-124. https://doi.org/10.23923/pap.psicol2019.2899
Crothers, L. M. y Levinson, E. M. (2004). Assessment of Bullying: A Review of Methods and Instruments. Journal of Counseling & Development, 82(4), 496-503. https://doi.org/10.1002/j.1556-6678.2004.tb00338.x
Egeberg, G., Thorvaldsen, S., Rønning, J. A. y Elstad, E. (2016). Digital Expectations and Experiences in Education. En The Impact of Cyberbullying and Cyber Harassment on Academic Achievement (pp. 183-204). Brill. https://doi.org/10.1007/9789463006484_012
Gabrielli, S., Rizzi, S., Carbone, S. y Donisi, V. (2020). A Chatbot-Based Coaching Intervention for Adolescents to Promote Life Skills: Pilot Study. JMIR Human Factors, 7(1), e16762. https://doi.org/10.2196/16762
Hadi, M. U., Qureshi, R., Shah, A., Irfan, M., Zafar, A., Shaikh, M. B., et al. (2023). A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical Usage. Authorea Preprints. https://doi.org/10.36227/techrxiv.23589741.v1
Hinduja, S. (2023, Mayo 10). Generative AI as a Vector for Harassment and Harm. Cyberbullying Research Center. https://bit.ly/42S9Mtf
Hinduja, S. y Patchin, J. W. (2008). Cyberbullying: An Exploratory Analysis of Factors Related to Offending and Victimization. Deviant Behavior, 29(2), 129-156. https://doi.org/10.1080/01639620701457816
Houbre, B., Tarquinio, C., Thuillier, I. y Hergott, E. (2006). Bullying among students and its consequences on health. European Journal of Psychology of Education, 21(2), 183-208. https://doi.org/10.1007/BF03173576
Iwendi, C., Srivastava, G., Khan, S. y Maddikunta, P. K. R. (2023). Cyberbullying detection solutions based on deep learning architectures. Multimedia Systems, 29(3), 1839-1852. https://doi.org/10.1007/s00530-020-00701-5
Kargutkar, S. M. y Chitre, V. (2020). A Study of Cyberbullying Detection Using Machine Learning Techniques. En 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) (pp. 734-739). IEEE. https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000137
Kowalski, R. M. y Limber, S. P. (2007). Electronic Bullying Among Middle School Students. Journal of Adolescent Health, 41(6, Supplement), S22-S30. https://doi.org/10.1016/j.jadohealth.2007.08.017
Koyuturk, C., Yavari, M., Theophilou, E., Bursic, S., Donabauer, G., Telari, A., et al. (2023). Developing effective educational Chatbots with ChatGPT prompts: insights from preliminary tests in a case study on social media literacy (with appendix). arXiv preprint arXiv:2306.10645. https://doi.org/10.48550/arXiv.2306.10645
Lafrance St-Martin, L. I. y Villeneuve, S. (2024). The uses of chatbots in the context of children and teenagers bullying: a systematic literature review. Cogent Education, 11(1), 2312032. https://doi.org/10.1080/2331186X.2024.2312032
Landstedt, E. y Persson, S. (2014). Bullying, cyberbullying, and mental health in young people. Scandinavian Journal of Public Health, 42(4), 393-399. https://doi.org/10.1177/1403494814525004
Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I. y Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), 100790. https://doi.org/10.1016/j.ijme.2023.100790
Lucas-Molina, B., Pérez-Albéniz, A., Solbes-Canales, I., Ortuño-Sierra, J. y Fonseca-Pedrero, E. (2022). Bullying, Cyberbullying and Mental Health: The Role of Student Connectedness as a School Protective Factor. Psychosocial Intervention, 31(1), 33 41. https://doi.org/10.5093/PI2022A1
Malik, T., Hughes, L., Dwivedi, Y. K. y Dettmer, S. (2023). Exploring the Transformative Impact of Generative AI on Higher Education. En M. Janssen, L. Pinheiro, R. Matheus, F. Frankenberger, Y. K. Dwivedi, I. O. Pappas, y M. Mäntymäki (Eds.), New Sustainable Horizons in Artificial Intelligence and Digital Solutions (pp. 69-77). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-50040-4_6
Mohamadi, S., Mujtaba, G., Le, N., Doretto, G. y Adjeroh, D. A. (2023). ChatGPT in the Age of Generative AI and Large Language Models: A Concise Survey. arXiv preprint arXiv:2307.04251. https://doi.org/10.48550/arXiv.2307.04251
Muzamil, M. y Shah, G. (2016). Cyberbullying and Self-Perceptions of Students Associated With Their Academic Performance. International Journal of Education and Development Using ICT, 12(3), 79-92. https://bit.ly/3TfWvY3
Naser, M. Z. y Alavi, A. H. (2023). Error Metrics and Performance Fitness Indicators for Artificial Intelligence and Machine Learning in Engineering and Sciences. Architecture, Structures and Construction, 3(4), 499-517. https://doi.org/10.1007/s44150-021-00015-8
Noblia, V., Renato, A. C. y Gershanik, T. (2022). Anonimato, pseudonimia y delitos en las redes sociales: una propuesta multidimensional de la lingüística forense para la identificación de autoría. Revista Latinoamericana de Estudios Del Discurso, 22(1), 122-142. https://doi.org/10.35956/v.22.n1.2022.p.122-142
Pichel, R., Feijóo, S., Isorna, M., Varela, J. y Rial, A. (2022). Analysis of the relationship between school bullying, cyberbullying, and substance use. Children and Youth Services Review, 134, 106369. https://doi.org/10.1016/j.childyouth.2022.106369
Reynoso, T. M., González, B. M. y López, A. S. (2021). Ciberbullying, brecha digital y habilidades digitales para ciberconvivencia: descripción en estudiantes de bachillerato. Voces de la educación, 6(12), 22-44. https://www.revista.vocesdelaeducacion.com.mx/index.php/voces/article/view/386
Schneider, S. K., O’donnell, L., Stueve, A. y Coulter, R. W. S. (2012). Cyberbullying, School Bullying, and Psychological Distress: A Regional Census of High School Students. American Journal of Public Health, 102(1), 171-177. https://doi.org/10.2105/AJPH.2011.300308
Schulenberg, K., Li, L., Freeman, G., Zamanifard, S. y McNeese, N. J. (2023). Towards Leveraging AI-based Moderation to Address Emergent Harassment in Social Virtual Reality. En A. Schmidt, K. Väänänen, T. Goyal, P. O. Kristensson, A. Peters, S. Mueller, J. R. Williamson, y M. L. Wilson (Eds.), Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-17). Association for Computing Machinery. https://doi.org/10.1145/3544548.3581090
Sidiropoulos, D. y Anagnostopoulos, C.-N. (2024). Applications, challenges and ethical issues of AI and ChatGPT in education. arXiv preprint arXiv:2402.07907. https://doi.org/10.48550/arXiv.2402.07907
Tesfagergish, S. G. y Damaševi?ius, R. (2024). Explainable Artificial Intelligence for Combating Cyberbullying. En K. K. Patel, K. C. Santosh, A. Patel, y A. Ghosh (Eds.), Soft Computing and Its Engineering Applications (pp. 54-67). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-53731-8_5
Tokunaga, R. S. (2010). Following you home from school: A critical review and synthesis of research on cyberbullying victimization. Computers in Human Behavior, 26(3), 277-287. https://doi.org/10.1016/j.chb.2009.11.014
Tukey, J. W. (1993). Exploratory Data Analysis: Past, Present and Future. Defense Technical Information Center. https://bit.ly/3SZ1nj6
Ventura, P. D. B., Rodríguez-García, A. M. y Reche, J. M. S. (2018). Incidencia del ciberbullying en adolescentes de 11 a 17 años en Portugal. Edutec. Revista Electrónica de Tecnología Educativa, (64), 82-98. https://doi.org/10.21556/edutec.2018.64.1029
Wang, J., Iannotti, R. J. y Nansel, T. R. (2009). School Bullying Among Adolescents in the United States: Physical, Verbal, Relational, and Cyber. Journal of Adolescent Health, 45(4), 368-375. https://doi.org/10.1016/j.jadohealth.2009.03.021
Ybarra, M. L. y Mitchell, K. J. (2004). Youth engaging in online harassment: associations with caregiver–child relationships, Internet use, and personal characteristics. Journal of Adolescence, 27(3), 319-336. https://doi.org/10.1016/j.adolescence.2004.03.007
Yousef, W. S. M. y Bellamy, A. (2015). The Impact of Cyberbullying on the Self-Esteem and Academic Functioning of Arab American Middle and High School Students. Electronic Journal of Research in Educational Psychology, 13(3), 463-482. https://doi.org/10.14204/ejrep.37.15011

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Received: 2024-02-28 | Reviewed: 2025-01-30 | Accepted: 2025-03-04 | Online First: 2025-04-18 | Published: 2025-04-20

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Dr. Ruben Nicolas-Sans., Rocío Navarro Martínez. (2025). Percepción y detección del ciberacoso: comparativa entre la comunidad educativa y las GenAI. Comunicar, 33(80). 10.58262/C80-2025-01

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