Abstract
The digital age has expanded social media and online forums, allowing free expression for nearly 45% of the global population. Yet, it has also fueled online harassment, bullying, and harmful behaviors like hate speech and toxic comments across social networks, messaging apps, and gaming communities. Studies show 65% of parents notice hostile online behavior, and one-third of adolescents in mobile games experience bullying. A substantial volume of abusive content is generated and shared daily, not only on the surface web but also within dark web forums. Creators of abusive comments often employ specific words or coded phrases to evade detection and conceal their intentions. To address these challenges, we propose a hybrid deep learning model that integrates BERT, CNN, and LSTM architectures with a ReLU activation function to detect abusive language across multiple online platforms, including YouTube comments, online forum discussions, and dark web posts. The model demonstrates strong performance on a diverse and imbalanced dataset containing 77,620 abusive and 272,214 non-abusive text samples (ratio 1:3.5), achieving approximately 99% across evaluation metrics such as Precision, Recall, Accuracy, F1-score, and AUC. This approach effectively captures semantic, contextual, and sequential patterns in text, enabling robust detection of abusive content even in highly skewed datasets, as encountered in real-world scenarios.
Abstract (translated)
数字时代扩展了社交媒体和在线论坛的范围，让全球近45%的人口能够自由表达观点。然而，它也助长了网络骚扰、欺凌以及诸如仇恨言论和有毒评论等有害行为，在社交网络、消息应用程序及游戏社区中广泛存在。研究表明，65%的家长注意到孩子的网上敌对行为，而三分之一的游戏青少年遭受过霸凌。每天有大量的恶意内容在表面网络和暗网论坛中生成并传播。恶意评论的创建者经常使用特定词汇或编码短语来逃避检测，并隐藏其意图。 为应对这些挑战，我们提出了一种结合BERT、CNN（卷积神经网络）和LSTM（长短期记忆网络）架构，并采用ReLU激活函数的混合深度学习模型，以在YouTube评论、在线论坛讨论以及暗网帖子等多个在线平台上检测恶意语言。该模型在一个包含77,620条恶意内容和272,214条非恶意文本样本（比例为1:3.5）的多样化且不平衡的数据集上表现出色，其精确度、召回率、准确率、F1分数及AUC等评价指标均达到了约99%。此方法能够有效捕捉到文本中的语义、上下文和序列模式，即使在现实世界中常见的高度偏斜数据集中也能稳健地检测出恶意内容。
URL
https://arxiv.org/abs/2603.09984
