Detection of Cyberbullying in social networks with deep learning based on CNN and LSTM neural network
Subject Areas : New technologies in distributed systems and algorithmic computing
Mohsen Eghbali
1
,
Kamal Mirzaie
2
*
,
Reza Aziz
3
1 - Department of Computer Engineering, Maybod Branch, Islamic Azad University, Maybod, Iran
2 - Department of Computer Engineering, Maybod Branch, Islamic Azad University, Maybod, Iran
3 - Department of Computer Engineering, Maybod Branch, Islamic Azad University, Maybod, Iran
Keywords: Social network, Cyberbullying, Deep learning, Convolutional neural network, LSTM neural network,
Abstract :
One of the promising approaches in cyberbullying detection is machine learning and deep learning algorithms. Nevertheless, detecting cyberbullying in social networks(SN) is complicated. Machine and deep learning methods for detecting cyberbullying in social networks can have a lot of errors. In this manuscript, to detect cyberbullying, the primary features of the text are extracted using three feature extraction methods including GloVe, Word2Vec, and TF-IDF methods. In the second phase, feature selection is done using the JSO algorithm, and finally, the essential features are considered as input to the 1DCNN and LSTM methods. Experiments are conducted on Twitter and Facebook datasets to detect cyberbullying. The results show that the accuracy, sensitivity(recall), and precision of the proposed method(JSO-CNN-LSTM) in detecting cyberbullying on Twitter are 98.23%, 97.86%, and 97.73%. The results demonstrate that the proposed method is more accurate than CNN, LSTM, and BERT methods in detecting cyberbullying.
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