In today's digital world, cyberbullying is a serious problem that can harm the mental and physical health of people who use social media. This paper explains just how serious cyberbullying is and how it really affects indi-viduals exposed to it. It also stresses how important it is to find better ways to detect cyberbullying so that online spaces can be safer. Plus, it talks about how making more accurate tools to spot cyberbullying will be really helpful in the future. Our paper introduces a deep learning-based ap-proach, primarily employing BERT and BiLSTM architectures, to effective-ly address cyberbullying. This approach is designed to analyse large vol-umes of posts and predict potential instances of cyberbullying in online spaces. Our results demonstrate the superiority of the hateBERT model, an extension of BERT focused on hate speech detection, among the five mod-els, achieving an accuracy rate of 89.16%. This research is a significant con-tribution to "Computational Intelligence for Social Transformation," prom-ising a safer and more inclusive digital landscape.

翻译：在当今数字世界中，网络暴力已成为一个严重威胁社交媒体用户身心健康的突出问题。本文阐述了网络暴力的严重性及其对受害者的真实影响，强调提升网络暴力检测能力对构建安全数字环境的关键意义，并指出未来开发更精准的检测工具将具有重要价值。我们提出了一种基于深度学习的解决方案，主要采用BERT和BiLSTM架构来有效应对网络暴力。该方法可分析海量帖文内容，预测网络空间中的潜在霸凌行为。实验结果显示，在五种模型中，专注于仇恨言论检测的hateBERT模型（BERT的扩展版本）表现最优，准确率达到89.16%。这项研究为"面向社会转型的计算智能"领域做出了重要贡献，为实现更安全、更具包容性的数字环境提供了有力支撑。