Adapting AI Algorithms for Dynamic Moderation in Multilingual Social Platforms
Keywords:
AI moderation, multilingual NLP, dynamic content filtering, cultural sensitivity, scalable algorithms, hate speech detection, misinformation control, Author Name, Scopus, Springer, Journal Name, Wissira, Journal Short Form, Wissira Press, Wissira Research Lab, Research Gate, SSRN, ISSN, Academia, UGC Care, PubMed, WOSAbstract
The rapid expansion of multilingual social platforms has introduced significant challenges in content moderation due to diverse linguistic, cultural, and contextual variations. This study focuses on adapting AI algorithms for dynamic moderation, aiming to balance scalability, fairness, and efficiency. Leveraging natural language processing (NLP) and deep learning, the research proposes a multilingual framework that accommodates real-time language detection, cultural sensitivity analysis, and contextual moderation. Case studies on real-world platforms validate the system's performance in detecting hate speech, misinformation, and harmful content. The results demonstrate enhanced accuracy and adaptability compared to traditional moderation systems, paving the way for more inclusive and scalable solutions.
