Author Information
Dhruvesh Vaghasiya, Nirma University, IndiaAman Deep Singh, Nirma University, India
Dev Detroja, Nirma University, India
Vedant Vaghasiya, Nirma University, India
Abstract
The proliferation of hate speech and toxic remarks in online communities presents considerable challenges to individuals, organisations, and society. This research examines the ramifications of detrimental communications and suggests an automated method for their identification utilising sophisticated machine learning (ML) and natural language processing (NLP) techniques. We examine multiple models, including deep learning architectures like BERT and GPT, to improve the precision of hate speech detection from extensive datasets obtained from platforms such as Twitter and Reddit. The study examines the complications involved with identifying hate speech, such as contextual reliance, sarcasm, and dataset biases, which frequently result in false positives and negatives. We provide a systematic review to assess current methodologies and their efficacy, while highlighting ethical considerations and the practical application of our approach. Our findings underscore significant deficiencies in existing research and propose new avenues for creating more efficient algorithms for identifying harmful information, thereby fostering healthier online environments.
Paper Information
Conference: BAMC2025Stream: Linguistics
This paper is part of the BAMC2025 Conference Proceedings (View)
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To cite this article:
Vaghasiya D., Singh A., Detroja D., & Vaghasiya V. (2025) Automated Detection of Hate Speech and Toxic Comments Using Machine Learning and Natural Language Processing ISSN: 2435-9475 – The Barcelona Conference on Arts, Media & Culture 2025: Official Conference Proceedings (pp. 143-154) https://doi.org/10.22492/issn.2435-9475.2025.15
To link to this article: https://doi.org/10.22492/issn.2435-9475.2025.15
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