Recognize Hate Speech On Twitter Using Machine Learning

Yashaswini, Kini and Chitra, K and Harilakshmi, V.M. (2024) Recognize Hate Speech On Twitter Using Machine Learning. Journal of Innovation and Technology, 2024 (28). pp. 1-6. ISSN 2805-5179

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Abstract

Convolutional Neural Network (CNN) is a frequent-deep learning algorithm that is powerful in classifying image and text data, the system analyses individual tweets in order to determine if it contains hate speech. The occurrence of offensive speech in online forums poses significant challenges to maintaining a safe and inclusive digital environment. This study addresses these challenges by developing a hate speech recognition system ML methods, specifically CNN algorithms aimed primarily at analysing hate speech in tweets, attempting to increased resource efficiency and accuracy, its system analyses textual content in the tweet and produces and indicates whether it contains hate speech and determines the percentage of intolerance speech present in the tweet. The results of this study highlight the power of CNN-based strategies in preventing cyberbullying and promoting healthy digital discourse.

Item Type:	Article
Uncontrolled Keywords:	Hate-Speech Detection, Machine Learning, Convolutional Neural Network (CNN), Social Networks Analysis, Text Classification
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General)
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	27 Nov 2024 06:51
Last Modified:	27 Nov 2024 06:51
URI:	http://eprints.intimal.edu.my/id/eprint/2059

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