{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T01:02:07Z","timestamp":1774746127196,"version":"3.50.1"},"reference-count":0,"publisher":"Zarqa University","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IAJIT"],"published-print":{"date-parts":[[2024]]},"abstract":"<jats:p>Cyberbullying causes significant harm, especially among adolescents and young adults. With the growth of social media, online harassment through platforms like Facebook and Twitter has also proliferated rapidly. Though social networks have reporting mechanisms, the volume of user-generated content makes manual moderation infeasible. This necessitates automated detection systems that can accurately identify cyberbullying at scale. Recent advances in deep learning provide promising techniques for text classification tasks. This paper explores Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and transformer models like Bidirectional Encoder Representations from Transformers (BERT) for cyberbullying detection in social networks. The models are evaluated on a benchmark dataset containing 11,000 Facebook comments labeled as clean or cyberbullying. Extensive experiments demonstrate that BERT achieves the highest accuracy of 87.3% followed by a hierarchical CNN-LSTM architecture with 86.5% accuracy. The former benefits from bidirectional context modeling using self-attention while the latter combines the strengths of convolutional layers and LSTMs. The results verify the effectiveness of deep learning methodologies for this problem. However, enhancements in multilingual, multimodal support and adversarial robustness are required. Testing on diverse platforms and content along with user privacy considerations remain as future research directions. This empirical study provides useful insights to build robust cyberbullying detection systems.<\/jats:p>","DOI":"10.34028\/iajit\/21\/6\/9","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T07:39:25Z","timestamp":1729496365000},"source":"Crossref","is-referenced-by-count":9,"title":["Cyberbullying Detection in Social Networks Using Deep Learning"],"prefix":"10.34028","volume":"21","author":[{"given":"Hayel","family":"Khafajeh","sequence":"first","affiliation":[]}],"member":"19944","published-online":{"date-parts":[[2024]]},"container-title":["The International Arab Journal of Information Technology"],"original-title":[],"language":"en","deposited":{"date-parts":[[2024,11,10]],"date-time":"2024-11-10T06:16:04Z","timestamp":1731219364000},"score":1,"resource":{"primary":{"URL":"https:\/\/iajit.org\/upload\/files\/Cyberbullying-Detection-in-Social-Networks-Using-Deep-Learning.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":0,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024]]},"published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.34028\/iajit\/21\/6\/9","archive":["Internet Archive"],"relation":{},"ISSN":["2309-4524","1683-3198"],"issn-type":[{"value":"2309-4524","type":"electronic"},{"value":"1683-3198","type":"print"}],"subject":[],"published":{"date-parts":[[2024]]}}}