Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/32472
Author(s): | Ramos, G. Batista, F. Ribeiro, R. Fialho, P. Moro, S. Fonseca, A. Guerra, R. Carvalho, P. Marques, C. Silva, C. |
Date: | 2024 |
Title: | A comprehensive review on automatic hate speech detection in the age of the transformer |
Journal title: | Social Network Analysis and Mining |
Volume: | 14 |
Number: | 1 |
Reference: | Ramos, G., Batista, F., Ribeiro, R., Fialho, P., Moro, S., Fonseca, A., Guerra, R., Carvalho, P., Marques, C., & Silva, C. (2024). A comprehensive review on automatic hate speech detection in the age of the transformer. Social Network Analysis and Mining, 14(1), Article 204. https://doi.org/10.1007/s13278-024-01361-3 |
ISSN: | 1869-5450 |
DOI (Digital Object Identifier): | 10.1007/s13278-024-01361-3 |
Keywords: | Hate speech detection Machine learning Deep learning Transfer learning Transformers Literature review |
Abstract: | 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. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica CIS-RI - Artigos em revistas científicas internacionais com arbitragem científica CTI-RI - Artigos em revistas científicas internacionais com arbitragem científica ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica |
Files in This Item:
File | Size | Format | |
---|---|---|---|
article_106025.pdf | 1,25 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.