Open Access

ARTICLE

An Adaptive Hate Speech Detection Approach Using Neutrosophic Neural Networks for Social Media Forensics

Yasmine M. Ibrahim^1,2, Reem Essameldin³, Saad M. Darwish^1,*

1 Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, Alexandria, 21526, Egypt
2 Faculty of Computers and Information Technology, Egyptian E-Learning University (EELU), Giza, 12611, Egypt
3 Faculty of Computers and Data Science, Alexandria University, Alexandria, 21554, Egypt

* Corresponding Author: Saad M. Darwish. Email:

(This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)

Computers, Materials & Continua 2024, 79(1), 243-262. https://doi.org/10.32604/cmc.2024.047840

Received 20 November 2023; Accepted 04 January 2024; Issue published 25 April 2024

Abstract

Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due to the complex nature of language used in such platforms. Currently, several methods exist for classifying hate speech, but they still suffer from ambiguity when differentiating between hateful and offensive content and they also lack accuracy. The work suggested in this paper uses a combination of the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) to adjust the weights of two Multi-Layer Perceptron (MLPs) for neutrosophic sets classification. During the training process of the MLP, the WOA is employed to explore and determine the optimal set of weights. The PSO algorithm adjusts the weights to optimize the performance of the MLP as fine-tuning. Additionally, in this approach, two separate MLP models are employed. One MLP is dedicated to predicting degrees of truth membership, while the other MLP focuses on predicting degrees of false membership. The difference between these memberships quantifies uncertainty, indicating the degree of indeterminacy in predictions. The experimental results indicate the superior performance of our model compared to previous work when evaluated on the Davidson dataset.

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Keywords

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