Integrating AI and Social Sciences to Address Online Abusive Language and Hate Speech

Bibliographic Data

ID4388762
AuthorsPradeep Kumar Roy (0000-0001-5513-2834, Indian Institute of Management Nagpur India, corresponding author)
Year2025
Volume76
Issue259
Pages243-255
Publication date2025-07-27
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Social Science Journal (JOURNAL)
Journal identifiersISSN: 0020-8701 • E-ISSN: 1468-2451
PublisherWiley (PUBLISHER • GB)
DOI10.1111/issj.70003
OpenAlexW4412683614
LanguageEN
References cited34

The rise in social media has led to an increase in abusive language online, necessitating effective solutions. This study introduces an automated model utilizing advanced frameworks for detecting and filtering abusive language and hate speech-related text in social media posts. The proposed model is trained on a publicly available dataset and uses machine learning (ML) and deep learning (DL) models with features extracted from pre-trained embedding vectors. The DL-based model outperformed state-of-the-art models. Human expertise is also used to validate misclassification. The model's performance was validated on a benchmark dataset, confirming its advantages over traditional frameworks. The proposed system can efficiently detect abusive, hate and offensive words with 89% accuracy and replace them with asterisks (*) in real-time, fostering respectful conversations on social platforms. This research addresses the sociocultural impact of online hate speech and its regulation through technology. By bridging AI with social concerns, the study offers a robust framework to support healthier online discourse and reduce the prevalence of harmful content on digital platforms

Linguistics · Media studies · Sociology · Hate Speech and Cyberbullying Detection · Philosophy · Psychology

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