Detecting cyberbullying in Spanish texts through deep learning techniques

TL;DR

This paper presents a deep learning-based model trained on a Spanish Twitter corpus to automatically detect cyberbullying expressions, including insults and hate speech, in social media texts.

Contribution

It introduces a new Spanish cyberbullying corpus and a convolutional neural network model for automatic detection of harmful online expressions.

Findings

The model accurately identifies various forms of cyberbullying.

The corpus enhances resources for Spanish cyberbullying detection.

Deep learning proves effective for multilingual online abuse detection.

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.