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Autores principales: Cumba-Armijos, Paúl, Riofrío-Luzcando, Diego, Rodríguez-Arboleda, Verónica, Carrión-Jumbo, Joe
Formato: Preprint
Publicado: 2025
Materias:
Acceso en línea:https://arxiv.org/abs/2512.19899
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author Cumba-Armijos, Paúl
Riofrío-Luzcando, Diego
Rodríguez-Arboleda, Verónica
Carrión-Jumbo, Joe
author_facet Cumba-Armijos, Paúl
Riofrío-Luzcando, Diego
Rodríguez-Arboleda, Verónica
Carrión-Jumbo, Joe
contents Recent recollected data suggests that it is possible to automatically detect events that may negatively affect the most vulnerable parts of our society, by using any communication technology like social networks or messaging applications. This research consolidates and prepares a corpus with Spanish bullying expressions taken from Twitter in order to use them as an input to train a convolutional neuronal network through deep learning techniques. As a result of this training, a predictive model was created, which can identify Spanish cyberbullying expressions such as insults, racism, homophobic attacks, and so on.
format Preprint
id arxiv_https___arxiv_org_abs_2512_19899
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Detecting cyberbullying in Spanish texts through deep learning techniques
Cumba-Armijos, Paúl
Riofrío-Luzcando, Diego
Rodríguez-Arboleda, Verónica
Carrión-Jumbo, Joe
Human-Computer Interaction
Machine Learning
Recent recollected data suggests that it is possible to automatically detect events that may negatively affect the most vulnerable parts of our society, by using any communication technology like social networks or messaging applications. This research consolidates and prepares a corpus with Spanish bullying expressions taken from Twitter in order to use them as an input to train a convolutional neuronal network through deep learning techniques. As a result of this training, a predictive model was created, which can identify Spanish cyberbullying expressions such as insults, racism, homophobic attacks, and so on.
title Detecting cyberbullying in Spanish texts through deep learning techniques
topic Human-Computer Interaction
Machine Learning
url https://arxiv.org/abs/2512.19899