Number of items at this level: 1.
2025
Article
Subjects > Engineering
Subjects > Comunication
Europe University of Atlantic > Research > Articles and books
Fundación Universitaria Internacional de Colombia > Research > Articles and books
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Articles and books
University of La Romana > Research > Scientific Production
Abierto
Inglés
The proliferation of damaging content on social media in today’s digital environment has increased the need for efficient hate speech identification systems. A thorough examination of hate speech detection methods in a variety of settings, such as code-mixed, multilingual, visual, audio, and textual scenarios, is presented in this paper. Unlike previous research focusing on single modalities, our study thoroughly examines hate speech identification across multiple forms. We classify the numerous types of hate speech, showing how it appears on different platforms and emphasizing the unique difficulties in multi-modal and multilingual settings. We fill research gaps by assessing a variety of methods, including deep learning, machine learning, and natural language processing, especially for complicated data like code-mixed and cross-lingual text. Additionally, we offer key technique comparisons, suggesting future research avenues that prioritize multi-modal analysis and ethical data handling, while acknowledging its benefits and drawbacks. This study attempts to promote scholarly research and real-world applications on social media platforms by acting as an essential resource for improving hate speech identification across various data sources.
Raza Ur Rehman, Hafiz Muhammad and Saleem, Mahpara and Jhandir, Muhammad Zeeshan and Silva Alvarado, Eduardo René and Garay, Helena and Ashraf, Imran
UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, eduardo.silva@funiber.org, helena.garay@uneatlantico.es, UNSPECIFIED
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