Ciência-IUL
Publicações
Descrição Detalhada da Publicação
Título Revista
Social Network Analysis and Mining
Ano (publicação definitiva)
2024
Língua
Inglês
País
Reino Unido
Mais Informação
Web of Science®
Scopus
Google Scholar
Abstract/Resumo
The rapid proliferation of hate speech on social media poses significant challenges to maintaining a safe and inclusive digital environment. This paper presents a comprehensive review of automatic hate speech detection methods, with a particular focus on the evolution of approaches from traditional machine learning and deep learning models to the more advanced Transformer-based architectures. We systematically analyze over 100 studies, comparing the effectiveness, computational requirements, and applicability of various techniques, including Support Vector Machines, Long Short-Term Memory networks, Convolutional Neural Networks, and Transformer models like BERT and its multilingual variants. The review also explores the datasets, languages, and sources used for hate speech detection, noting the predominance of English-focused research while highlighting emerging efforts in low-resource languages and cross-lingual detection using multilingual Transformers. Additionally, we discuss the role of generative and multi-task learning models as promising avenues for future development. While Transformer-based models consistently achieve state-of-the-art performance, this review underscores the trade-offs between performance and computational cost, emphasizing the need for context-specific solutions. Key challenges such as algorithmic bias, data scarcity, and the need for more standardized benchmarks are also identified. This review provides crucial insights for advancing the field of hate speech detection and shaping future research directions.
Agradecimentos/Acknowledgements
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Palavras-chave
Hate speech detection,Machine learning,Deep learning,Transfer learning,Transformers,Literature review
Classificação Fields of Science and Technology
- Ciências da Computação e da Informação - Ciências Naturais
- Engenharia Civil - Engenharia e Tecnologia
- Ciências da Comunicação - Ciências Sociais
Registos de financiamentos
Referência de financiamento | Entidade Financiadora |
---|---|
CERV-2021-EQUAL (101049306) | Comissão Europeia |