用于检测社交媒体网络欺凌的深度神经网络模型的比较分析

IF 0.4 Q4 ENGINEERING, MULTIDISCIPLINARY
Sivadi Balakrishna, Yerra Gopi, Vijender Kumar Solanki
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引用次数: 2

摘要

社交媒体的使用已经增加,它包括积极和消极的影响。考虑到跟踪、骚扰等各种网络欺凌方法对社交媒体平台的滥用,应该有预防性的方法来控制这些行为,避免精神压力。这些额外的单词将扩大词汇表的大小,并影响算法的性能。因此,我们提出了LSTM、BI-LSTM、RNN、BI-RNN、GRU、BI-GRU等变体深度学习模型来检测社交媒体中的网络欺凌。这些模型被应用在推特上,观察了这些模型的公众评论数据和性能,并获得了90.4%的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative analysis on deep neural network models for detection of cyberbullying on Social Media
Social media usage has been increased and it consists of both positive and negative effects. By considering the misusage of social media platforms by various cyberbullying methods like stalking, harassment there should be preventive methods to control these and to avoid mental stress. These extra words will expand the size of the vocabulary and influence the performance of the algorithm. Therefore, we come up with variant deep learning models like LSTM, BI-LSTM, RNN, BI-RNN, GRU, BI-GRU to detect cyberbullying in social media. These models are applied on Twitter, public comments data and performance were observed for these models and obtained improved accuracy of 90.4%.
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Ingenieria Solidaria
Ingenieria Solidaria ENGINEERING, MULTIDISCIPLINARY-
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