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.

通过深度学习方法，特别是采用 BERT 和 BiLSTM 架构，我们的研究介绍了一种针对网络霸凌问题的深度学习方法，可以有效地分析大量帖子并预测潜在的网络霸凌行为。结果表明，我们的 hateBERT 模型在五个模型中表现卓越，达到了 89.16% 的准确率，为实现更安全和更包容的数字化环境作出了重要贡献。

保障社交空间：利用深度学习消除网络欺凌

Securing Social Spaces: Harnessing Deep Learning to Eradicate  Cyberbullying

In this paper, we introduce HateBERT, a re-trained BERT model for abusive
language detection in English. The model was trained on RAL-E, a large-scale
dataset of Reddit comments in English from communities banned for being
offensive, abusive, or hateful that we have collected and made available to the
public. We present the results of a detailed comparison between a general
pre-trained language model and the abuse-inclined version obtained by
retraining with posts from the banned communities on three English datasets for
offensive, abusive language and hate speech detection tasks. In all datasets,
HateBERT outperforms the corresponding general BERT model. We also discuss a
battery of experiments comparing the portability of the generic pre-trained
language model and its corresponding abusive language-inclined counterpart
across the datasets, indicating that portability is affected by compatibility
of the annotated phenomena.

介绍了 HateBERT，这是一个在大规模 Reddit 评论数据集（RAL-E）上重新训练过的 BERT 模型，用于检测英语中的辱骂语言。在三个英语数据集中，加入了被封禁社区的帖子进行重新训练，检测攻击性、辱骂性语言和仇恨言论，结果表明 HateBERT 优于通用 BERT 模型，并进行了相关的实验。