Cyberbullying is disturbing and troubling online misconduct. It appears in various forms and is usually in a textual format in most social networks. Intelligent systems are necessary for automated detection of these incidents. Some of the recent experiments have tackled this issue with traditional machine learning models. Most of the models have been applied to one social network at a time. The latest research has seen different models based on deep learning algorithms make an impact on the detection of cyberbullying. These detection mechanisms have resulted in efficient identification of incidences while others have limitations of standard identification versions. This paper performs an empirical analysis to determine the effectiveness and performance of deep learning algorithms in detecting insults in Social Commentary. The following four deep learning models were used for experimental results, namely: Bidirectional Long Short-Term Memory (BLSTM), Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). Data pre-processing steps were followed that included text cleaning, tokenization, stemming, Lemmatization, and removal of stop words. After performing data pre-processing, clean textual data is passed to deep learning algorithms for prediction. The results show that the BLSTM model achieved high accuracy and F1-measure scores in comparison to RNN, LSTM, and GRU. Our in-depth results shown which deep learning models can be most effective against cyberbullying when directly compared with others and paves the way for future hybrid technologies that may be employed to combat this serious online issue.

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基于深度学习架构的网络欺凌检测解决方案

网络欺凌是令人不安和令人不安的在线不当行为。它以各种形式出现，在大多数社交网络中通常以文本格式出现。智能系统对于自动检测这些事件是必要的。最近的一些实验已经用传统的机器学习模型解决了这个问题。大多数模型一次应用于一个社交网络。最新研究表明，基于深度学习算法的不同模型对网络欺凌的检测产生了影响。这些检测机制导致了对事件的有效识别，而其他机制则具有标准识别版本的局限性。本文进行了实证分析，以确定深度学习算法在检测社会评论中的侮辱方面的有效性和性能。实验结果使用了以下四种深度学习模型，即：双向长短期记忆（BLSTM）、门控循环单元（GRU）、长短期记忆（LSTM）和循环神经网络（RNN）。遵循数据预处理步骤，包括文本清理、标记化、词干提取、词形还原和去除停用词。在执行数据预处理后，干净的文本数据被传递给深度学习算法进行预测。结果表明，与 RNN、LSTM 和 GRU 相比，BLSTM 模型实现了较高的准确率和 F1-measure 分数。我们的深入研究结果表明，与其他模型直接比较时，哪些深度学习模型可以最有效地对抗网络欺凌，并为未来可用于解决这一严重在线问题的混合技术铺平了道路。