{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T17:09:15Z","timestamp":1767373755276,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T00:00:00Z","timestamp":1739318400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union-NextGenerationEU","award":["PE00000014"],"award-info":[{"award-number":["PE00000014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/fi17020084","type":"journal-article","created":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T12:12:16Z","timestamp":1739362336000},"page":"84","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Cyberbullying Detection, Prevention, and Analysis on Social Media via Trustable LSTM-Autoencoder Networks over Synthetic Data: The TLA-NET Approach"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7104-6415","authenticated-orcid":false,"given":"Alfredo","family":"Cuzzocrea","sequence":"first","affiliation":[{"name":"iDEA Lab, University of Calabria, 87036 Rende, Italy"},{"name":"Department of Computer Science, University of Paris City, 75013 Paris, France"}]},{"given":"Mst Shapna","family":"Akter","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Oakland University, Rochester, MI 48309, USA"}]},{"given":"Hossain","family":"Shahriar","sequence":"additional","affiliation":[{"name":"Center for Cybersecurity, University of West Florida, Pensacola, FL 32514, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3594-9534","authenticated-orcid":false,"given":"Pablo","family":"Garc\u00eda Bringas","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Deusto, 48007 Bilbao, Bizkaia, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105283","DOI":"10.1016\/j.engappai.2022.105283","article-title":"Identification of Cyber Harassment and Intention of Target Users on Social Media Platforms","volume":"115","author":"Abarna","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"101314","DOI":"10.1016\/j.copsyc.2022.101314","article-title":"Cyberbullying via Social Media and Well-being","volume":"45","author":"Giumetti","year":"2022","journal-title":"Curr. Opin. Psychol."},{"key":"ref_3","first-page":"351","article-title":"A Study on Positive and Negative Effects of Social Media on Society","volume":"5","author":"Akram","year":"2017","journal-title":"Int. J. Comput. Sci. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s10464-009-9232-1","article-title":"The Good, the Bad, and the Ugly: Domestic Violence Survivors\u2019 Experiences with their Informal Social Networks","volume":"43","author":"Trotter","year":"2009","journal-title":"Am. J. Community Psychol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.chb.2018.12.021","article-title":"Automatic Cyberbullying Detection: A Systematic Review","volume":"93","author":"Rosa","year":"2019","journal-title":"Comput. Hum. Behav."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.procs.2015.03.085","article-title":"Online Social Network Bullying Detection Using Intelligence Techniques","volume":"45","author":"Nandhini","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/s00038-014-0644-9","article-title":"Longitudinal Associations Between Cyberbullying Perpetration and Victimization and Problem Behavior and Mental Health Problems in Young Australians","volume":"60","author":"Hemphill","year":"2015","journal-title":"Int. J. Public Health"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ioannou, A., Blackburn, J., Siringhini, G., De Chrisiofaro, E., Kouriellis, N., Sirivianos, M., and Zaphiris, P. (June, January 30). From Risk Factors to Detection and Intervention: A Metareview and Practical Proposal for Research on Cyberbullying. Proceedings of the 2017 IEEE IST-Africa Week Conference, Windhoek, Namibia.","DOI":"10.23919\/ISTAFRICA.2017.8102355"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Al Mazari, A. (2013, January 27\u201328). Cyber-Bullying Taxonomies: Definition, Forms, Consequences and Mitigation Strategies. Proceedings of the 5th IEEE International Conference on Computer Science and Information Technology, Amman, Jordan.","DOI":"10.1109\/CSIT.2013.6588770"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1016\/j.procs.2021.01.207","article-title":"Accurate Cyberbullying Detection and Prevention on Social Media","volume":"181","author":"Perera","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"104356","DOI":"10.1016\/j.compedu.2021.104356","article-title":"Cyberbullying in Elementary and Middle School Students: A Systematic Review","volume":"176","author":"Evangelio","year":"2022","journal-title":"Comput. Educ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"171","DOI":"10.2105\/AJPH.2011.300308","article-title":"Cyberbullying, School Bullying, and Psychological Distress: A Regional Census of High School Students","volume":"102","author":"Schneider","year":"2012","journal-title":"Am. J. Public Health"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/j.neucom.2021.12.022","article-title":"Tackling Cyber-Aggression: Identification and Fine-Grained Categorization of Aggressive Texts on Social Media Using Weighted Ensemble of Transformers","volume":"490","author":"Sharif","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.neucom.2021.08.148","article-title":"Classifying Cybergrooming for Child Online Protection Using Hybrid Machine Learning Model","volume":"484","author":"Isaza","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_15","unstructured":"Ramana, D., and Reddy, T.H. (2021, January 25\u201326). Detection of Online Hate in Social Media Platforms for Twitter Data: A Prefatory Step. Proceedings of the 9th International Conference on Frontiers in Intelligent Computing: Theory and Applications, Aizawl, India."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"101386","DOI":"10.1016\/j.csl.2022.101386","article-title":"Hate Speech and Offensive Language Detection in Dravidian Languages Using Deep Ensemble Framework","volume":"75","author":"Roy","year":"2022","journal-title":"Comput. Speech Lang."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.tcs.2022.06.020","article-title":"Deep Learning and Natural Language Processing in Computation for Offensive Language Detection in Online Social Networks by Feature Selection and Ensemble Classification Techniques","volume":"943","author":"Anand","year":"2022","journal-title":"Theor. Comput. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102537","DOI":"10.1016\/j.ijinfomgt.2022.102537","article-title":"Are You a Cyberbully On Social Media? Exploring the Personality Traits Using a Fuzzy-set Configurational Approach","volume":"66","author":"Hossain","year":"2022","journal-title":"Int. J. Inf. Manag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/380995.380999","article-title":"Support Vector Machines: Hype or Hallelujah?","volume":"2","author":"Bennett","year":"2000","journal-title":"SIGKDD Explor."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Alotaibi, M., Alotaibi, B., and Razaque, A. (2021). A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media. Electronics, 10.","DOI":"10.20944\/preprints202110.0070.v1"},{"key":"ref_21","unstructured":"Ahmed, M.F., Mahmud, Z., Biash, Z.T., Ryen, A.A.N., Hossain, A., and Ashraf, F.B. (2021). Cyberbullying Detection Using Deep Neural Network From Social Media Comments in Bangla Language. arXiv."},{"key":"ref_22","unstructured":"Kumari, K., and Singh, J.P. (2020, January 21\u201323). AI_ML_NIT_Patna @ TRAC-2: Deep Learning Approach for Multi-lingual Aggression Identification. Proceedings of the 2nd Workshop on Trolling, Aggression and Cyberbullying, Marseille, France."},{"key":"ref_23","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1002\/grl.50288","article-title":"GPT2: Empirical Slant Delay Model for Radio Space Geodetic Techniques","volume":"40","author":"Lagler","year":"2013","journal-title":"Geophys. Res. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Graves, A. (2012). Long Short-Term Memory. Supervised Sequence Labelling with Recurrent Neural Networks, Springer.","DOI":"10.1007\/978-3-642-24797-2"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"102282","DOI":"10.1016\/j.ijinfomgt.2020.102282","article-title":"Forecasting and Anomaly Detection Approaches Using LSTM and LSTM Autoencoder Techniques with the Applications in Supply Chain Management","volume":"57","author":"Nguyen","year":"2021","journal-title":"Int. J. Inf. Manag."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1109\/TIM.2019.2910342","article-title":"LSTM-based Auto-encoder Model for ECG Arrhythmias Classification","volume":"69","author":"Hou","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Said Elsayed, M., Le-Khac, N.A., Dev, S., and Jurcut, A.D. (2020, January 16\u201320). Network Anomaly Detection Using LSTM Based Autoencoder. Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks, Alicante, Spain.","DOI":"10.1145\/3416013.3426457"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1017\/S1351324916000334","article-title":"Word2vec","volume":"23","author":"Church","year":"2017","journal-title":"Nat. Lang. Eng."},{"key":"ref_31","unstructured":"Ahmed, M.F., Mahmud, Z., Biash, Z.T., Ryen, A.A.N., Hossain, A., and Ashraf, F.B. (Mendeley Data, Version 1, 2021). Bangla Online Comments Dataset, Mendeley Data, Version 1."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhou, Z.H. (2021). Machine Learning, Springer Nature.","DOI":"10.1007\/978-981-15-1967-3"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Rokach, L., and Maimon, O. (2005). Decision Trees. Data Mining and Knowledge Discovery Handbook, Springer.","DOI":"10.1007\/0-387-25465-X_9"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s11749-016-0481-7","article-title":"A Random Forest Guided Tour","volume":"25","author":"Biau","year":"2016","journal-title":"Test"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2395","DOI":"10.1161\/CIRCULATIONAHA.106.682658","article-title":"Logistic Regression","volume":"117","author":"LaValley","year":"2008","journal-title":"Circulation"},{"key":"ref_36","unstructured":"Almeida, L.B. (2020). Multilayer Perceptrons. Handbook of Neural Computation, CRC Press. chapters 1\u20132."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"70701","DOI":"10.1109\/ACCESS.2019.2918354","article-title":"Predicting Cyberbullying on Social Media in the Big Data Era Using Machine Learning Algorithms: Review of Literature and Open Challenges","volume":"7","author":"Hussain","year":"2019","journal-title":"IEEE Access"},{"key":"ref_38","first-page":"465","article-title":"FedBully: A Cross-Device Federated Approach for Privacy Enabled Cyber Bullying Detection using Sentence Encoders","volume":"12","author":"Shetty","year":"2023","journal-title":"J. Cyber Secur. Mobil."},{"key":"ref_39","unstructured":"Dong, Y., Li, Y., Zhao, D., Shen, G., and Zeng, Y. (2023, January 10\u201316). Bullying10K: A Large-Scale Neuromorphic Dataset towards Privacy-Preserving Bullying Recognition. Proceedings of the Annual Conference on Neural Information Processing Systems, New Orleans, LA, USA."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Akter, M.S., Shahriar, H., Cuzzocrea, A., Wu, F., and Rodriguez-Cardenas, J. (2023, January 15\u201318). A Trustable LSTM-Autoencoder Network for Cyberbullying Detection on Social Media Using Synthetic Data. Proceedings of the 2023 IEEE International Conference on Big Data, Sorrento, Italy.","DOI":"10.1109\/BigData59044.2023.10386719"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"125641","DOI":"10.1016\/j.eswa.2024.125641","article-title":"Automatic Detection of Cyberbullying Behaviour on Social Media using Stacked Bi-Gru Attention with BERT Model","volume":"262","author":"Mali","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"90158","DOI":"10.1109\/ACCESS.2024.3420131","article-title":"Toward Multi-Modal Approach for Identification and Detection of Cyberbullying in Social Networks","volume":"12","author":"Faheem","year":"2024","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"58950","DOI":"10.1109\/ACCESS.2024.3386637","article-title":"RoBERTaNET: Enhanced RoBERTa Transformer Based Model for Cyberbullying Detection With GloVe Features","volume":"12","author":"Jamjoom","year":"2024","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"100027","DOI":"10.1016\/j.nlp.2023.100027","article-title":"A Robust Hybrid Machine Learning Model for Bengali Cyber Bullying Detection in Social Media","volume":"4","author":"Akhter","year":"2023","journal-title":"Nat. Lang. Process. J."},{"key":"ref_45","unstructured":"Bhattacharya, S., Singh, S., Kumar, R., Bansal, A., Bhagat, A., Dawer, Y., Lahiri, B., and Ojha, A.K. (2020). Developing a Multilingual Annotated Corpus of Misogyny and Aggression. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.chb.2013.10.036","article-title":"Cyberbullying on Social Network Sites. An Experimental Study into Bystanders\u2019 Behavioural Intentions to Help the Victim or Reinforce the Bully","volume":"31","author":"Bastiaensens","year":"2014","journal-title":"Comput. Hum. Behav."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"101850","DOI":"10.1016\/j.datak.2020.101850","article-title":"Interpretable Anomaly Prediction: Predicting Anomalous Behavior in Industry 4.0 Settings Via Regularized Logistic Regression Tools","volume":"130","author":"Langone","year":"2020","journal-title":"Data Knowl. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Barkwell, K.E., Cuzzocrea, A., Leung, C.K., Ocran, A.A., Sanderson, J.M., Stewart, J.A., and Wodi, B.H. (2018, January 10\u201313). Big Data Visualisation and Visual Analytics for Music Data Mining. Proceedings of the 22nd IEEE International Conference Information Visualisation, Fisciano, Italy.","DOI":"10.1109\/iV.2018.00048"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1109\/TCSS.2021.3049232","article-title":"BullyNet: Unmasking Cyberbullies on Social Networks","volume":"8","author":"Srinath","year":"2021","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Yu, B., Cuzzocrea, A., Jeong, D.H., and Maydebura, S. (2012, January 13\u201316). On Managing Very Large Sensor-Network Data Using Bigtable. Proceedings of the 12th IEEE\/ACM International Symposium on Cluster, Cloud and Grid Computing, Ottawa, ON, Canada.","DOI":"10.1109\/CCGrid.2012.150"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"31","DOI":"10.5334\/dsj-2024-031","article-title":"The Optimization of n-Gram Feature Extraction Based on Term Occurrence for Cyberbullying Classification","volume":"23","author":"Setiawan","year":"2024","journal-title":"Data Sci. J."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"20507","DOI":"10.1007\/s11042-023-16402-w","article-title":"An Efficient Automated Multi-Modal Cyberbullying Detection using Decision Fusion Classifier on Social Media Platforms","volume":"83","author":"Singh","year":"2024","journal-title":"Multimed. Tools Appl."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/2\/84\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:32:05Z","timestamp":1760027525000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/2\/84"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,12]]},"references-count":52,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["fi17020084"],"URL":"https:\/\/doi.org\/10.3390\/fi17020084","relation":{},"ISSN":["1999-5903"],"issn-type":[{"type":"electronic","value":"1999-5903"}],"subject":[],"published":{"date-parts":[[2025,2,12]]}}}