DETECTION AND ANALYSIS OF ONLINE HATE SPEECH USING ARTIFICIAL INTELLIGENCE

Abstract

The ubiquity of social media and the interne has facilitated unprecedented connec-tivity and information exchange, but it has also given rise to a troubling phenomenon: Online Hate Speech. A comprehensive examination of Online Hate Speech Detection is explored, along with its various dimensions, impacts, and detection methodologies. The pervasive nature of OHS within the digital age, emphasizing its detrimental ef-fects on social cohesion, individual well-being, and democratic discourse is explored. Drawing from scholarly literature and empirical evidence, the urgent need for robust interventions to counter OHS is underscored. The multifaceted nature of hate speech is elucidated, encompassing various forms of discrimination, prejudice, and incitement to violence. Special attention is paid to the role of anonymity, echo chambers, and algorithmic amplification in perpetuating Online Hate Speech within online ecosystems. Against this backdrop, innovative Ar-tificial Intelligence techniques for Online Hate Speech detection, aiming to empower stakeholders with tools to identify and address hate speech effectively are proposed. Firstly, the HateSwarm feature engineering technique is introduced as a novel approach to feature selection, leveraging bio-inspired algorithms to prioritize salient linguistic cues indicative of hate speech. By enhancing the interpretability and generalizability of hate speech detection models, HateSwarm offers a promising avenue for improving algorithmic performance in real-world settings. Building upon this foundation, the Hate-Detector model is proposed as a sophis-ticated tool for multilingual hate speech detection. Integrating state-of-the-art natu-ral language processing techniques, including Bidirectional Encoder Representations from Transformers (BERT) and Multi-Layer Perceptron (MLP) architecture, HateDe-tector demonstrates high accuracy in identifying hate speech across diverse linguistic contexts. Through rigorous validation on annotated datasets in multiple languages, the model showcases its effectiveness in addressing the challenges of linguistic variation and cultural specificity inherent in hate speech detection. In parallel, the scarcity of standardized multilingual hate speech datasets is ad-dressed introducing an innovative methodology for dataset construction. Leveraging advanced techniques such as BERT embeddings, clustering, and topic modeling, this approach facilitates the systematic compilation of annotated hate speech data across languages and platforms, laying the groundwork for more robust hate speech detection models with broader applicability.