Comparative analysis on deep neural network models for detection of cyberbullying on Social Media

Author:

Balakrishna Sivadi,Gopi Yerrakula,Solanki Vijender Kumar

Abstract

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%.

Publisher

Universidad Cooperativa de Colombia- UCC

Subject

General Engineering

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Detection of Cyberbullying in Social Media Texts Using Explainable Artificial Intelligence;Communications in Computer and Information Science;2024

2. Cyber Analyzer—A Machine Learning Approach for the Detection of Cyberbullying—A Survey;IoT Based Control Networks and Intelligent Systems;2023-11-28

3. Multimodal Cyberbullying Detection on Social Media: Review and Challenges;2023 International Conference on Integration of Computational Intelligent System (ICICIS);2023-11-01

4. Impact of Facebook, TikTokand Instagram advertising onthe Arlington VA community;Ingenieria Solidaria;2023-06-14

5. Identification and Labeling of Textual Cyberbullying using BiLSTM and BERT;2023 International Conference on Networking and Communications (ICNWC);2023-04-05

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