Deep learning-based profanity detection in social media: a survey in the context of toxic language detection

The spread of toxic and profane language across online platforms has necessitated the development of advanced automated detection systems to maintain safe online environments. This survey comprehensively explores the progression of profanity language detection techniques, focusing on datasets, feature extraction methods, and model architectures. Through a thorough examination of key developments in the field, the study outlines the evolution of detection systems across four distinct phases. It offers a taxonomy of approaches that have shaped the current landscape. We review various high-performing models, highlighting their effectiveness in addressing challenges such as context understanding, multilingual data, and identifying subtle forms of toxicity. The article further explores the strengths of these models, including their ability to capture long-range dependencies and nuanced features, while also discussing their limitations, such as computational efficiency and generalization across datasets. By analyzing the current state of toxic language detection and the latest advancements in feature extraction and modeling techniques, we provide valuable insights for researchers and practitioners. Additionally, the article outlines future research directions, emphasizing the importance of lightweight, adaptive models and real-time optimization techniques to enable scalable, practical deployment across diverse platforms and environments.