Journal
IEEE ACCESS
Volume 10, Issue -, Pages 25857-25871Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3153675
Keywords
Cyberbullying; Blogs; Feature extraction; Support vector machines; Recurrent neural networks; Training; Numerical models; Cyber-bullying; tweet classification; Dolphin Echolocation algorithm; Elman recurrent neural networks; short text topic modeling; cyberbullying detection; social media
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This paper proposes a hybrid deep learning model called DEA-RNN to detect cyberbullying on the Twitter platform. The experimental results show that DEA-RNN outperforms other algorithms in all aspects.
Cyberbullying (CB) has become increasingly prevalent in social media platforms. With the popularity and widespread use of social media by individuals of all ages, it is vital to make social media platforms safer from cyberbullying. This paper presents a hybrid deep learning model, called DEA-RNN, to detect CB on Twitter social media network. The proposed DEA-RNN model combines Elman type Recurrent Neural Networks (RNN) with an optimized Dolphin Echolocation Algorithm (DEA) for fine-tuning the Elman RNN's parameters and reducing training time. We evaluated DEA-RNN thoroughly utilizing a dataset of 10000 tweets and compared its performance to those of state-of-the-art algorithms such as Bi-directional long short term memory (Bi-LSTM), RNN, SVM, Multinomial Naive Bayes (MNB), Random Forests (RF). The experimental results show that DEA-RNN was found to be superior in all the scenarios. It outperformed the considered existing approaches in detecting CB on Twitter platform. DEA-RNN was more efficient in scenario 3, where it has achieved an average of 90.45% accuracy, 89.52% precision, 88.98% recall, 89.25% F1-score, and 90.94% specificity.
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