Deep Learning-based Natural Language Processing Methods Comparison for Presumptive Detection of Cyberbullying in Social Networks

Due to TIC development in the last years, users have managed to satisfy many social experiences through several digital media like blogs, web and especially social networks. However, not all social media users have had good experiences with these media. Since there are malicious users that are able to cause negative psychological effects over people, this is called cyberbullying. For this reason, social networks such as Twitter are looking to implement models based on deep learning or machine learning capable of recognizing harassing comments on their platforms. However, most of these models are focused on the use of English language and there are very few models adapted for Spanish language. This is why, in this paper we propose the evaluation of an RNN+LSTM neural network, as well as a BERT model through sentiment analysis, to perform the detection of cyberbullying based on Spanish language for Ecuadorian accounts of the social network Twitter. The results obtained show a balance between execution time and accuracy obtained for the RNN + LSTM model. In addition, evaluations of comments that are not explicitly offensive show a better performance for the BERT model, which outperforms its counterpart by 20%.