A Trustable LSTM-Autoencoder Network for Cyberbullying Detection on
  Social Media Using Synthetic Data
Mst Shapna Akter Hossain Shahriar Alfredo Cuzzocrea
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 Short-Term 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.