Cyberbullying Detection Model for Arabic Text Using Deep Learning
Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
World scientific connect
Abstract
. In the new era of digital communications, cyberbullying is a significant concern for society.
Cyberbullying can negatively impact stakeholders and can vary from psychological to pathological,
such as self-isolation, depression and anxiety potentially leading to suicide. Hence, detecting any act
of cyberbullying in an automated manner will be helpful for stakeholders to prevent any unfortunate
results from the victim’s perspective. Data-driven approaches, such as machine learning (ML), par ticularly deep learning (DL), have shown promising results. However, the meta-analysis shows that
ML approaches, particularly DL, have not been extensively studied for the Arabic text classification
of cyberbullying. Therefore, in this study, we conduct a performance evaluation and comparison for
various DL algorithms (LSTM, GRU, LSTM-ATT, CNN-BLSTM, CNN-LSTM and LSTM-TCN) on
different datasets of Arabic cyberbullying to obtain more precise and dependable findings. As a result
of the models’ evaluation, a hybrid DL model is proposed that combines the best characteristics of the
baseline models CNN, BLSTM and GRU for identifying cyberbullying. The proposed hybrid model
improves the accuracy of all the studied datasets and can be integrated into different social media sites
to automatically detect cyberbullying from Arabic social datasets. It has the potential to significantly
reduce cyberbullying. The application of DL to cyberbullying detection problems within Arabic text
classification can be considered a novel approach due to the complexity of the problem and the tedious
process involved, besides the scarcity of relevant research studies.
Description
Keywords
Text mining; deep learning; convolutional neural network; classification; categorisation;
natural language processing; Arabic language.
Citation
Albayari, R., Abdallah, S. and Shaalan, K. (2024) “Cyberbullying Detection Model for Arabic Text Using Deep Learning,” Journal of Information & Knowledge Management [Preprint].