Detecting hate in diversity: a survey of multilingual code-mixed image and video analysis
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| Publicado en: | Journal of Big Data vol. 12, no. 1 (May 2025), p. 109 |
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| Publicado: |
Springer Nature B.V.
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 024 | 7 | |a 10.1186/s40537-025-01167-w |2 doi | |
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| 045 | 2 | |b d20250501 |b d20250531 | |
| 245 | 1 | |a Detecting hate in diversity: a survey of multilingual code-mixed image and video analysis | |
| 260 | |b Springer Nature B.V. |c May 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Multilingualism | ||
| 653 | |a Modal analysis | ||
| 653 | |a Hate speech | ||
| 653 | |a Social networks | ||
| 653 | |a Deep learning | ||
| 653 | |a Machine learning | ||
| 653 | |a Natural language processing | ||
| 653 | |a Digital media | ||
| 653 | |a Big Data | ||
| 653 | |a Social media | ||
| 653 | |a Data processing | ||
| 653 | |a Ethics | ||
| 653 | |a Code switching | ||
| 653 | |a Multimodality | ||
| 653 | |a Mass media images | ||
| 653 | |a Video recordings | ||
| 653 | |a Research | ||
| 653 | |a Research applications | ||
| 653 | |a Speech perception | ||
| 773 | 0 | |t Journal of Big Data |g vol. 12, no. 1 (May 2025), p. 109 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3203359516/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3203359516/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |