Cyberbullying Detection, Prevention, and Analysis on Social Media via Trustable LSTM-Autoencoder Networks over Synthetic Data: The TLA-NET Approach

Alfredo Cuzzocrea (), Mst Shapna Akter, Hossain Shahriar and Pablo García Bringas
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Alfredo Cuzzocrea: iDEA Lab, University of Calabria, 87036 Rende, Italy
Mst Shapna Akter: Department of Computer Science and Engineering, Oakland University, Rochester, MI 48309, USA
Hossain Shahriar: Center for Cybersecurity, University of West Florida, Pensacola, FL 32514, USA
Pablo García Bringas: Faculty of Engineering, University of Deusto, 48007 Bilbao, Bizkaia, Spain

Future Internet, 2025, vol. 17, issue 2, 1-21

Abstract: The plague of cyberbullying on social media exerts a dangerous influence on human lives. Due to the fact that online social networks continue to daily expand, the proliferation of hate speech is also growing. Consequentially, distressing content is often implicated in the onset of depression and suicide-related behaviors. In this paper, we propose an innovative framework, named as the trustable LSTM-autoencoder network (TLA NET), which is designed for the detection of cyberbullying on social media by employing synthetic data. We introduce a state-of-the-art method for the automatic production of translated data, which are aimed at tackling data availability issues. Several languages, including Hindi and Bangla, continue to face research limitations due to the absence of adequate datasets. Experimental identification of aggressive comments is carried out via datasets in Hindi, Bangla, and English. By employing TLA NET and traditional models, such as long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), the LSTM-autoencoder, Word2vec, bidirectional encoder representations from transformers (BERT), and the Generative Pre-trained Transformer 2 (GPT-2), we perform the experimental identification of aggressive comments in datasets in Hindi, Bangla, and English. In addition to this, we employ evaluation metrics that include the F1-score, accuracy, precision, and recall, to assess the performance of the models. Our model demonstrates outstanding performance across all the datasets by achieving a remarkable 99% accuracy and positioning itself as a frontrunner when compared to previous works that make use of the dataset featured in this research.

Keywords: cyber-bullying; deep learning; neural networks; natural language processing (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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