IAES International Journal of Artificial Intelligence (IJ-AI) Vol. 13, No. 1, March 2024, pp. 500~508 ISSN: 2252-8938, DOI: 10.11591/ijai.v13.i1.pp500-508  500 Journal homepage: http://ijai.iaescore.com Word embedding for detecting cyberbullying based on recurrent neural networks Noor Haydar Shaker, Ban N. Dhannoon Department of Computer Science, College of Science, Al-Nahrain University, Baghdad, Iraq Article Info ABSTRACT Article history: Received Jan 27, 2023 Revised Mar 19, 2023 Accepted Mar 27, 2023 The phenomenon of cyberbullying has spread and has become one of the biggest problems facing users of social media sites and generated significant adverse effects on society and the victim in particular. Finding appropriate solutions to detect and reduce cyberbullying has become necessary to mitigate its negative impacts on society and the victim. Twitter comments on two datasets are used to detect cyberbullying, the first dataset was the Arabic cyberbullying dataset, and the second was the English cyberbullying dataset. Three different pre-trained global vectors (GloVe) corpora with different dimensions were used on the original and preprocessed datasets to represent the words. Recurrent neural networks (RNN), long short-term memory (LSTM), Bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and Bidirectional GRU (BiGRU) classifiers utilized, evaluated and compared. The GRU outperform other classifiers on both datasets; its accuracy on the Arabic cyberbullying dataset using the Arabic GloVe corpus of dimension equal to 256D is 87.83%, while the accuracy on the English datasets using 100 D pre- trained GloVe corpus is 93.38%. Keywords: Deep learning classifiers Gated recurrent unit GloVe word embedding Long short-term memory Recurrent neural networks This is an open access article under the CC BY-SA license. Corresponding Author: Noor Haydar Shaker Department of Computer Science, College of Science, Al-Nahrain University Baghdad, Iraq Email: noor.haidar21@ced.nahrainuniv.edu.iq 1. INTRODUCTION The development of technological technologies and the increase in the number of users of social media sites, including users who try to harm others, led to the spread of cyberbullying. Cyberbullying is a type of bullying in which one or more persons (the bully) purposefully and frequently cause harm to another person (the victim) through using technological technologies. Cyberbullies utilize technological technologies like mobile phones, computers, or other electronic devices to send emails, instant text messages, make comments on social media or in chat rooms, or otherwise to harass their victims [1], [2]. Cyberbullying may have serious and long-term consequences for its victims, like a physical, mental, and emotional impact on the victim that leaves them feeling scared, furious, humiliated, exhausted, or have symptoms such as headaches or stomach pains. When victims experience cyberbullying, they might start to feel ashamed, nervous, anxious, and insecure about what people say or think about them. This can lead to withdrawal from friends and family, and it may lead to the victim's suicide [3], [4]. So, it has become necessary to search for and find solutions to detect cyberbullying messages. Many attempts have been made in the field of artificial intelligence to detect the phenomenon of cyberbullying by using machine learning and deep learning techniques, and attempts are continuing to find the best results and appropriate solutions to detect this phenomenon to reduce the negative effects that generate in society, especially on the category of teenagers who are more exposed to cyberbullying than the rest category of society.