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Ramos, G., Batista, F., Ribeiro, R., Fialho, P., Moro, S., Fonseca, A....Silva, C. (2024). A comprehensive review on automatic hate speech detection in the age of the transformer. Social Network Analysis and Mining. 14 (1)

G. A. Ramos et al.,  "A comprehensive review on automatic hate speech detection in the age of the transformer", in Social Network Analysis and Mining, vol. 14, no. 1, 2024

@article{ramos2024_1732200280068,
	author = "Ramos, G. and Batista, F. and Ribeiro, R. and Fialho, P. and Moro, S. and Fonseca, A. and Guerra, R. and Carvalho, P. and Marques, C. and Silva, C.",
	title = "A comprehensive review on automatic hate speech detection in the age of the transformer",
	journal = "Social Network Analysis and Mining",
	year = "2024",
	volume = "14",
	number = "1",
	doi = "10.1007/s13278-024-01361-3",
	url = "https://link.springer.com/journal/13278"
}

TY  - JOUR
TI  - A comprehensive review on automatic hate speech detection in the age of the transformer
T2  - Social Network Analysis and Mining
VL  - 14
IS  - 1
AU  - Ramos, G.
AU  - Batista, F.
AU  - Ribeiro, R.
AU  - Fialho, P.
AU  - Moro, S.
AU  - Fonseca, A.
AU  - Guerra, R.
AU  - Carvalho, P.
AU  - Marques, C.
AU  - Silva, C.
PY  - 2024
SN  - 1869-5450
DO  - 10.1007/s13278-024-01361-3
UR  - https://link.springer.com/journal/13278
AB  - 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.
ER  -