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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