International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 3 May-June 2024 Submit your research before last 3 days of June to publish your research paper in the issue of May-June.

Deep Learning Approaches for Hate Speech Detection

Author(s) Ravi Tomar, Shaurya Pratap Singh, Siddhartha Verma, Amar Pal Yadav
Country India
Abstract The main goals of this project are to develop reliable hate speech identification models that can recognize derogatory terminology, prejudiced attitudes, and damaging stereotypes on a variety of internet platforms. To help the models train and generalize efficiently, the study focuses on using big datasets with a variety of content types, including both hate speech and non-hate speech. The findings of this study suggest that machine learning has the potential to mitigate the negative consequences of hate speech by using automated filtering and flagging tools. The study also highlights the need for continued research and development to improve the accuracy, uniformity, and transparency of hate speech detection systems, and ultimately to foster a safer online environment to encourage all people.
Keywords Hate Speech, Machine Learning, Offensive Language, Deep Learning neural networks
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-05-20
Cite This Deep Learning Approaches for Hate Speech Detection - Ravi Tomar, Shaurya Pratap Singh, Siddhartha Verma, Amar Pal Yadav - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.17073
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.17073
Short DOI https://doi.org/gtvt4p

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