ArticlePeerJ. Computer science2024
A survey on multi-lingual offensive language detection.
Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Multilingual sentiment analysis in restaurant reviews using aspect focused learning.Scientific reports · 2025Article
- Detection of offensive content in the Kazakh language using machine learning and deep learning approaches.PeerJ. Computer science · 2025Article
- Multilingual Hate Speech Detection: A Semi-Supervised Generative Adversarial Approach.Entropy (Basel, Switzerland) · 2024Article
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Authors and funding
7 authors.
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Abstract
The prevalence of offensive content on online communication and social media platforms is growing more and more common, which makes its detection difficult, especially in multilingual settings. The term "Offensive Language" encompasses a wide range of expressions, including various forms of hate speech and aggressive content. Therefore, exploring multilingual offensive content, that goes beyond a single language, focus and represents more linguistic diversities and cultural factors. By exploring multilingual offensive content, we can broaden our understanding and effectively combat the widespread global impact of offensive language. This survey examines the existing state of multilingual offensive language detection, including a comprehensive analysis on previous multilingual approaches, and existing datasets, as well as provides resources in the field. We also explore the related community challenges on this task, which include technical, cultural, and linguistic ones, as well as their limitations. Furthermore, in this survey we propose several potential future directions toward more efficient solutions for multilingual offensive language detection, enabling safer digital communication environment worldwide.
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