A Trustable LSTM-Autoencoder Network for Cyberbullying Detection on Social Media Using Synthetic Data
A Trustable LSTM-Autoencoder Network for Cyberbullying Detection on Social Media Using Synthetic Data
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DOI：
10.1109/bigdata59044.2023.10386719
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
Mst. Shapna Akter;Hossain Shahriar;A. Cuzzocrea
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文献类型：
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作者：
Mst. Shapna Akter;Hossain Shahriar;A. Cuzzocrea
社交媒体网络欺凌对人类生活产生有害影响。随着在线社交网络日益增长，仇恨言论的数量也在增加。此类可怕的内容可能会导致抑郁和与自杀相关的行为。本文提出了一种可信赖的 LSTM 自动编码器网络，用于使用合成数据检测社交媒体上的网络欺凌。我们展示了一种通过生成机器翻译数据来解决数据可用性困难的尖端方法。然而，由于缺乏数据集，印地语和孟加拉语等几种语言仍然缺乏足够的调查。我们使用所提出的模型和传统模型，包括长短期记忆 (LSTM)、双向长短期记忆 (BiLSTM)、LSTM-自动编码器、Word2vec、来自 Transformers 的双向编码器表示 (BERT) 和生成预训练 Transformer 2 (GPT-2) 模型，对印地语、孟加拉语和英语数据集进行了攻击性评论的实验识别。我们采用 f1 分数、准确性、精确度和召回率等评估指标来评估模型的性能。我们提出的模型在所有数据集上都优于所有模型，达到了 95% 的最高准确率。我们的模型在本文使用的数据集上的所有先前工作中取得了最先进的结果。
Social media cyberbullying has a detrimental effect on human life. As online social networking grows daily, the amount of hate speech also increases. Such terrible content can cause depression and actions related to suicide. This paper proposes a trustable LSTM-Autoencoder Network for cyberbullying detection on social media using synthetic data. We have demonstrated a cutting-edge method to address data availability difficulties by producing machine-translated data. However, several languages such as Hindi and Bangla still lack adequate investigations due to a lack of datasets. We carried out experimental identification of aggressive comments on Hindi, Bangla, and English datasets using the proposed model and traditional models, including Long Short-Term Memory (LSTM), Bidirectional Long ShortTerm Memory (BiLSTM), LSTM-Autoencoder, Word2vec, Bidirectional Encoder Representations from Transformers (BERT), and Generative Pre-trained Transformer 2 (GPT-2) models. We employed evaluation metrics such as f1-score, accuracy, precision, and recall to assess the models’ performance. Our proposed model outperformed all the models on all datasets, achieving the highest accuracy of 95%. Our model achieves state-of-the-art results among all the previous works on the dataset we used in this paper.