eprintid: 17788 rev_number: 9 eprint_status: archive userid: 2 dir: disk0/00/01/77/88 datestamp: 2025-05-12 23:30:10 lastmod: 2025-05-12 23:30:12 status_changed: 2025-05-12 23:30:10 type: article metadata_visibility: show creators_name: Raza Ur Rehman, Hafiz Muhammad creators_name: Saleem, Mahpara creators_name: Jhandir, Muhammad Zeeshan creators_name: Silva Alvarado, Eduardo René creators_name: Garay, Helena creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: eduardo.silva@funiber.org creators_id: helena.garay@uneatlantico.es creators_id: title: Detecting hate in diversity: a survey of multilingual code-mixed image and video analysis ispublished: pub subjects: uneat_eng subjects: uneat_mm divisions: uneatlantico_produccion_cientifica divisions: unincol_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: unic_produccion_cientifica divisions: uniromana_produccion_cientifica full_text_status: public keywords: Hate speech detection; Social media; Feature engineering; Deep learning; Multimodal data analysis abstract: 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 publication: Journal of Big Data volume: 12 number: 1 id_number: doi:10.1186/s40537-025-01167-w refereed: TRUE issn: 2196-1115 official_url: http://doi.org/10.1186/s40537-025-01167-w access: open language: en citation: 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 document_url: http://repositorio.uneatlantico.es/id/eprint/17788/1/s40537-025-01167-w.pdf