Title: Detecting cyberbullying in Spanish texts through deep learning techniques

Authors: Paúl Cumba-Armijos; Diego Riofrío-Luzcando; Verónica Rodríguez-Arboleda; Joe Carrión-Jumbo

Addresses: Digital School, SEK International University, Quito, Pichincha, Ecuador ' Digital School, SEK International University, Quito, Pichincha, Ecuador ' Digital School, SEK International University, Quito, Pichincha, Ecuador ' Digital School, SEK International University, Quito, Pichincha, Ecuador

Abstract: 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.

Keywords: cyberbullying; deep learning; convolutional neuronal network; Spanish; social networks.

DOI: 10.1504/IJDMMM.2022.125265

International Journal of Data Mining, Modelling and Management, 2022 Vol.14 No.3, pp.234 - 247

Received: 16 Feb 2021
Accepted: 12 Aug 2021
Published online: 05 Sep 2022 *

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