relation: http://repositorio.uneatlantico.es/id/eprint/17788/ canonical: http://repositorio.uneatlantico.es/id/eprint/17788/ title: Detecting hate in diversity: a survey of multilingual code-mixed image and video analysis creator: Raza Ur Rehman, Hafiz Muhammad creator: Saleem, Mahpara creator: Jhandir, Muhammad Zeeshan creator: Silva Alvarado, Eduardo René creator: Garay, Helena creator: Ashraf, Imran subject: Ingeniería subject: Comunicación description: 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. date: 2025-05 type: Artículo type: PeerReviewed format: text language: en rights: cc_by_nc_nd_4 identifier: http://repositorio.uneatlantico.es/id/eprint/17788/1/s40537-025-01167-w.pdf identifier: Artículo Materias > Ingeniería Materias > Comunicación Universidad Europea del Atlántico > Investigación > Artículos y libros Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Universidad de La Romana > Investigación > Producción Científica 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. metadata Raza Ur Rehman, Hafiz Muhammad; Saleem, Mahpara; Jhandir, Muhammad Zeeshan; Silva Alvarado, Eduardo René; Garay, Helena y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, eduardo.silva@funiber.org, helena.garay@uneatlantico.es, SIN ESPECIFICAR (2025) Detecting hate in diversity: a survey of multilingual code-mixed image and video analysis. Journal of Big Data, 12 (1). ISSN 2196-1115 relation: http://doi.org/10.1186/s40537-025-01167-w relation: doi:10.1186/s40537-025-01167-w language: en