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

作　　者：Yasmine M.Ibrahim Reem Essameldin Saad M.Darwish 

机构地区：[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

出　　处：《Computers, Materials & Continua》2024年第4期243-262,共20页计算机、材料和连续体（英文）

摘　　要：Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due tothe complex nature of language used in such platforms. Currently, several methods exist for classifying hatespeech, but they still suffer from ambiguity when differentiating between hateful and offensive content and theyalso 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 exploreand determine the optimal set of weights. The PSO algorithm adjusts the weights to optimize the performanceof the MLP as fine-tuning. Additionally, in this approach, two separate MLP models are employed. One MLPis dedicated to predicting degrees of truth membership, while the other MLP focuses on predicting degrees offalse membership. The difference between these memberships quantifies uncertainty, indicating the degree ofindeterminacy in predictions. The experimental results indicate the superior performance of our model comparedto previous work when evaluated on the Davidson dataset.

关 键 词：Hate speech detection whale optimization neutrosophic sets social media forensics 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]