# A Trustable LSTM-Autoencoder Network for Cyberbullying Detection on Social Media Using Synthetic Data

arXiv: https://arxiv.org/abs/2308.09722 · HTML: https://mlsanity.com/paper/2308.09722/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 55 references that are in the map.
- First posted on arXiv 15 Aug 2023.

## Builds on (most cited first, 9 of 9)

- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Multilingual Offensive Language Identification with Cross-lingual Embeddings](https://mlsanity.com/paper/2010.05324/) (2020)
- [Developing a Multilingual Annotated Corpus of Misogyny and Aggression](https://mlsanity.com/paper/2003.07428/) (2020)
- [Leveraging Multilingual Transformers for Hate Speech Detection](https://mlsanity.com/paper/2101.03207/) (2021)
- [Cyberbullying Detection Using Deep Neural Network from Social Media Comments in Bangla Language](https://mlsanity.com/paper/2106.04506/) (2021)
- [Handwritten Word Recognition using Deep Learning Approach: A Novel Way of Generating Handwritten Words](https://mlsanity.com/paper/2303.07514/) (2023)
- [Deep Learning Approach for Classifying the Aggressive Comments on Social Media: Machine Translated Data Vs Real Life Data](https://mlsanity.com/paper/2303.07484/) (2023)
- [Software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms](https://mlsanity.com/paper/2306.08060/) (2023)
- [KEIS@JUST at SemEval-2020 Task 12: Identifying Multilingual Offensive Tweets Using Weighted Ensemble and Fine-Tuned BERT](https://mlsanity.com/paper/2005.07820/) (2020)

## Abstract (arXiv)

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.