Valle Cano, Gloria delQuijano Sánchez, LaraLiberatore, FedericoGómez, Jesús2023-11-162023-11-162023-04-15Expert Systems with Applications, (2023), 216:119446, (17 p.).0957-4174https://hdl.handle.net/10016/38884Social media platforms have evolved into an online representation of our social interactions. We may use the resources they provide to analyze phenomena that occur within them, such as the development and viralization of offensive and hostile content. In today's polarized world, the escalating nature of this behavior is cause for concern in modern society. This research includes an in-depth examination of previous efforts and strategies for detecting and preventing hateful content on the social network Twitter, as well as a novel classification approach based on users' profiles, related social environment and generated tweets. This paper's contribution is threefold: (i) an improvement in the performance of the HaterNet algorithm, an expert system developed in collaboration with the Spanish National Office Against Hate Crimes of the Spanish State Secretariat for Security (Ministry of the Interior) that is capable of identifying and monitoring the evolution of hate speech on Twitter using an LTSM + MLP neural network architecture. To that end, a model based on BERT, HaterBERT, has been created and tested using HaterNet's public dataset, providing results that show a significant improvement; (ii) A methodology to create a user database in the form of a relational network to infer textual and centrality features. This contribution, SocialGraph, has been independently tested with various traditional Machine Learning and Deep Learning algorithms, demonstrating its usefulness in spotting haters; (iii) a final model, SocialHaterBERT, that integrates the previous two approaches by analyzing features other than those inherent in the text. Experiment results reveal that this last contribution greatly improves outcomes, establishing a new field of study that transcends textual boundaries, paving the way for future research in coupled models from a diachronic and dynamic perspective.17eng© 2022 The Author(s). Published by Elsevier Ltd.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync- nd/4.0/).Atribución-NoComercial-SinDerivadas 3.0 EspañaHate speechTwitterDeep learningSocial network analysisBidirectional encoder representations from transformersBERTTopic modelingresearch articleInformáticahttps://doi.org/10.1016/j.eswa.2022.119446open access111944617EXPERT SYSTEMS WITH APPLICATIONS216AR/0000033524