Advanced Approaches for Hate Speech Detection: A Machine and Deep Learning Investigation

Title

Advanced Approaches for Hate Speech Detection: A Machine and Deep Learning Investigation

Creator

Reghunathan A.; Singh S.; Gunavathi R.; Johnson A.

Description

The prevalence of online social media platforms has led to an alarming rise in the frequency of cyberbullying and hate speech. This study uses a variety of machine-learning approaches and deep- learning algorithms to identify hate speech. The goal is to create a thorough and successful method for locating and categorizing hate speech on online networks. Our suggested approach intends to deliver a comprehensive solution to address the urgent problem of cyberbullying and hate speech in the digital sphere by leveraging the strength of these cutting-edge techniques. We work to make social media users' online experiences safer and more welcoming by identifying and addressing such harmful online actions. Through rigorous experimentation, we evaluate the efficacy of these methodologies, ultimately revealing that the Bidirectional Gated Recurrent Unit (Bi-GRU) outperforms the other employed techniques. The Bi-GRU model demonstrates superior hate speech detection capabilities, substantiated by robust performance metrics. This research contributes to the field by providing empirical evidence that deep learning models, such as Bi-GRU, can significantly advance hate speech detection accuracy. The findings underscore the potential of leveraging advanced neural architectures in the pursuit of fostering a more inclusive and respectful digital space. 2024 IEEE.

Source

TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Bi-GRU; classification; deep learning; Hate speech; machine learning; NLP; RNN

Coverage

Reghunathan A., Christ (Deemed to Be University), School of Science, Lavasa, India; Singh S., Christ (Deemed to Be University), School of Science, Lavasa, India; Gunavathi R., Christ (Deemed to Be University), School of Science, Lavasa, India; Johnson A., Christ (Deemed to Be University), School of Science, Lavasa, India

Rights

Restricted Access

Relation

ISBN: 979-835038427-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/TQCEBT59414.2024.10545188

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204489011&doi=10.1109%2fTQCEBT59414.2024.10545188&partnerID=40&md5=a933df9e38c3107705aeec06e4f0f29c