SocialHaterBERT: A dichotomous approach for automatically detecting hate speech on Twitter through textual analysis and user profiles
EntityUAM. Departamento de Ingeniería Informática
10.1016/j.eswa.2022.119446Expert Systems With Applications 216 (2023): 119446
Funded byThe research of Quijano-Sánchez was conducted with financial support from the Spanish Ministry of Science and Innovation, grant PID2019-108965GBI00. The research of Liberatore is partially funded by the European Commission’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie, grant number MSCA-RISE 691161 (GEOSAFE), and the Government of Spain, grant MTM2015-65803-R. All the financial support is gratefully acknowledged
Projectinfo:eu-repo/grantAgreement/EC/H2020/691161; Gobierno de España. MTM2015-65803-R
SubjectsBERT; Deep learning; Hate speech; Social network analysis; Topic modeling; Twitter; Informática
Rights© 2022 The Author(s)
Esta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
Social 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
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Google Scholar:Valle Cano, Gloria del - Quijano Sánchez, Lara - Liberatore, Federico - Gómez, Jesús
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