Transformer network-based word embeddings approach for autonomous cyberbullying detection
Abstract
Purpose: Nowadays people are connected by social media like Facebook, Instagram, Twitter, YouTube and much more. Bullies take advantage of these social networks to share their comments. Cyberbullying is one typical kind of harassment by making aggressive comments, abuses to hurt the netizens. Social media is one of the areas where bullying happens extensively. Hence, it is necessary to develop an efficient and autonomous cyberbullying detection technique. Design/methodology/approach: In this paper, the authors proposed a transformer network-based word embeddings approach for cyberbullying detection. RoBERTa is used to generate word embeddings and Light Gradient Boosting Machine is used as a classifier. Findings: The proposed approach outperforms machine learning algorithms such as logistic regression, support vector machine and deep learning models such as word-level convolutional neural networks (word CNN) and character convolutional neural networks with short cuts (char CNNS) in terms of precision, recall, F1-score. Originality/value: One of the limitations of traditional word embeddings methods is context-independent. In this work, only text data are utilized to identify cyberbullying. This work can be extended to predict cyberbullying activities in multimedia environment like image, audio and video.
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Transformers are deep learning models based on attention that have shown effectiveness in detecting cyberbullying.
“Transformers are deep learning models based on attention that have shown effectiveness in detecting cyberbullying [113-115].”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [113]
Cites
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515 · see citing works
Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831 · see citing works
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983 · see citing works
Cyberbullying: Its nature and impact in secondary school pupils
Smith, P. K., Mahdavi, J., Carvalho, M., Fisher, S., Russell, S., & Tippett, N. (2008) · Journal of Child Psychology and Psychiatry · doi:10.1111/j.1469-7610.2007.01846.x · Cited by 3,639 · see citing works
Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter
Talat, Z., & Hovy, D. (2016) · North American Chapter of the Association for Computational Linguistics · doi:10.18653/v1/n16-2013 · Cited by 1,919 · see citing works
Social Media Cyberbullying Detection using Machine Learning
Hani, J., Nashaat, M., Ahmed, M., Emad, Z., Amer, E., & Mohammed, A. (2019) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2019.0100587 · Cited by 160 · see citing works
Cyberbullying Detection Based on Semantic-Enhanced Marginalized Denoising Auto-Encoder
Zhao, R., & Mao, K. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2531682 · Cited by 133 · see citing works
Cyberbullying, Psychosocial Adjustment, and Suicidal Ideation in Adolescence
Iranzo, B., Buelga, S., Cava, M. J., & Ortega-Barón, J. (2019) · Psychosocial Intervention · doi:10.5093/pi2019a5 · Cited by 124 · see citing works
Cyberbullying Detection with a Pronunciation Based Convolutional Neural Network
Zhang, X., Tong, J., Vishwamitra, N., Whittaker, E., Mazer, J. P., Kowalski, R., Hu, H., Luo, F., Macbeth, J., & Dillon, E. (2016) · 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla.2016.0132 · Cited by 118 · see citing works
Online Social Network Bullying Detection Using Intelligence Techniques
Nandhini, B. S., & Sheeba, J. I. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.03.085 · Cited by 115 · see citing works
Deep Learning Algorithm for Cyberbullying Detection
Al-Ajlan, M., & Ykhlef, M. (2018) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2018.090927 · Cited by 83 · see citing works
Domestic Violence Crisis Identification From Facebook Posts Based on Deep Learning
Subramani, S., Wang, H., Vu, H. Q., & Li, G. (2018) · IEEE Access · doi:10.1109/access.2018.2871446 · Cited by 80 · see citing works
Deep Learning for Multi-Class Identification From Domestic Violence Online Posts
Subramani, S., Michalska, S., Wang, H., Du, J., Zhang, Y., & Shakeel, H. (2019) · IEEE Access · doi:10.1109/access.2019.2908827 · Cited by 79 · see citing works
A multi-platform dataset for detecting cyberbullying in social media
Van Bruwaene, D., Huang, Q., & Inkpen, D. (2020) · Language Resources and Evaluation · doi:10.1007/s10579-020-09488-3 · Cited by 74 · see citing works
Vijayaprabakaran, K., & Sathiyamurthy, K. (2020) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2020.04.015 · Cited by 69 · see citing works
A new method of emotional analysis based on CNN–BiLSTM hybrid neural network
Liu, Z., Zhang, D., Luo, G.-Z., Lian, M., & Liu, B. (2020) · Cluster Computing · doi:10.1007/s10586-020-03055-9 · Cited by 67 · see citing works
A Machine Learning Approach to Comment Toxicity Classification
Chakrabarty, N. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-9042-5_16 · Cited by 55 · see citing works
Classifying and diacritizing Arabic poems using deep recurrent neural networks
Abandah, G. A., Khedher, M. Z., Abdel-Majeed, M., Mansour, H., Hulliel, S. F., & Bisharat, L. M. (2020) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2020.12.002 · Cited by 44 · see citing works
Classification of Cyber-Aggression Cases Applying Machine Learning
Gutiérrez-Esparza, G. O., Vallejo, M., & Hernández-Torruco, J. (2019) · Applied Sciences · doi:10.3390/app9091828 · Cited by 38 · see citing works
A Hybrid Deep Learning System of CNN and LRCN to Detect Cyberbullying from SNS Comments
Bu, S.-J., & Cho, S.-B. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-92639-1_47 · Cited by 30 · see citing works
Toward efficient and effective bullying detection in online social network
Wu, J., Wen, M., Lu, R., Li, B., & Li, J. (2020) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-019-00832-1 · Cited by 13 · see citing works
Patacsil, F. F. (2019) · International Journal of Intelligent Systems and Applications · doi:10.5815/ijisa.2019.11.05 · Cited by 10 · see citing works
Shylaja, S. S., Narayanan, A., Venugopal, A. U., & Prasad, A. (2017) · International conference natural language processing · Cited by 3 · see citing works
Catching them red-handed: Real-time Aggression Detection on Social Media
Herodotou, H., Chatzakou, D., & Kourtellis, N. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.4323436 · Cited by 1 · see citing works
Catching them red-handed: Real-time Aggression Detection on Social Media
Herodotou, H., Chatzakou, D., & Kourtellis, N. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.4323435 · Cited by 1 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805
Flagged first, then by citations
Cited by
Sorted by citations
Sarker, I. H. (2022) · Annals of Data Science · doi:10.1007/s40745-022-00444-2 · Cited by 298 · see citing works
Murshed, B. A. H., Abawajy, J., Mallappa, S., Saif, M. A. N., & Al-Ariki, H. D. E. (2022) · IEEE Access · doi:10.1109/access.2022.3153675 · Cited by 137 · see citing works
“Pericherla and Ilavarasan [33] suggested a transformer network-based word embedding model to classify CB tweets.”
InfluentialA Review on Deep-Learning-Based Cyberbullying Detection
Hasan, M. T., Hossain, M. A. E., Mukta, M. S. H., Akter, A., Ahmed, M., & Islam, S. (2023) · Future Internet · doi:10.3390/fi15050179 · Cited by 103 · see citing works
“Transformers are deep learning models based on attention that have shown effectiveness in detecting cyberbullying [113–115].”
BackgroundSarker, I. H. (2023) · Security and Privacy · doi:10.1002/spy2.295 · Cited by 101 · see citing works
Session-based cyberbullying detection in social media: A survey
Yi, P., & Zubiaga, A. (2023) · Online Social Networks and Media · doi:10.1016/j.osnem.2023.100250 · Cited by 55 · see citing works
FAEO-ECNN: cyberbullying detection in social media platforms using topic modelling and deep learning
Murshed, B. A. H., Suresha, Abawajy, J., Saif, M. A. N., Abdulwahab, H. M., & Ghanem, F. A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15372-3 · Cited by 39 · see citing works
Gutiérrez‐Batista, K., Gómez-Sánchez, J., & Fernandez‐Basso, C. (2024) · Social Network Analysis and Mining · doi:10.1007/s13278-024-01291-0 · Cited by 36 · see citing works
Li, T., Zeng, Z., Li, Q., & Sun, S. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103651 · Cited by 27 · see citing works
Shahzad, K., Khan, S. A., Javeed, A. M. D., & Iqbal, A. (2024) · Global Knowledge, Memory and Communication · doi:10.1108/gkmc-11-2023-0422 · Cited by 13 · see citing works
Azmi, N. S. A. B. N., Ptaszynski, M. E., Masui, F., Eronen, J., & Nowakowski, K. (2025) · Natural Language Processing Journal · doi:10.1016/j.nlp.2025.100132 · Cited by 13 · see citing works
Deep Learning-Based Cyberbullying Detection in Kurdish Language
Badawi, S. S. (2024) · The Computer Journal · doi:10.1093/comjnl/bxae024 · Cited by 11 · see citing works
Padma, P., & Rao, G. S. N. (2024) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.623 · Cited by 11 · see citing works
Pérez-Rodríguez, P., Machimbarrena, J. M., Ortega-Barón, J., Díaz‐López, A., Caba‐Machado, V., & González‐Cabrera, J. M. (2024) · Acta Psychologica · doi:10.1016/j.actpsy.2024.104280 · Cited by 6 · see citing works
Prathaban, B. P., Vaishnavi, T., Ramesh, A. P., Sujaritha, M., Rama, R. S., & Jeffin Gracewell, J. (2024) · International Journal of Information Technology · doi:10.1007/s41870-024-02350-7 · Cited by 3 · see citing works
Chinivar, S., Roopa, M. S., Arunalatha, J. S., & Venugopal, K. R. (2022) · IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE) · doi:10.1109/icwite57052.2022.10176223 · Cited by 3 · see citing works
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Rohini, D. S., & Ramchander, M. (2023) · International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT) · doi:10.1109/icaiccit60255.2023.10466143 · Cited by 3 · see citing works
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Hibatullah, H., Ballı, T., & Yetkin, E. F. (2025) · Entertainment Computing · doi:10.1016/j.entcom.2025.101009 · Cited by 2 · see citing works
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Akinyemi, J. D., Ibitoye, A., Oyewale, C. T., & Onifade, O. F. W. (2023) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-36118-0_40 · Cited by 2 · see citing works
Saim, M., Rizvi, R. M., & Khan, M. K. (2023) · International Conference on Recent Advances in Science and Engineering Technology (ICRASET) · doi:10.1109/icraset59632.2023.10419938 · Cited by 1 · see citing works
Prestiliano, J., Azhari, A., & Nurwidyantoro, A. (2025) · International Seminar on Intelligent Technology and Its Applications · doi:10.1109/isitia66279.2025.11137495 · Cited by 1 · see citing works
A deep contrastive granular learning transformer for coarse-grained text representationPreprint
Izadkhah, H., & Behzadidoost, R. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5472934
Lieharyani, D. C. U., Hutahaean, J., Nugraha, A. C., Suprihanto, & Pribadi, D. S. (2025) · E3S Web of Conferences · doi:10.1051/e3sconf/202566401004
X-MCB-Net: Explainable multimodal Cyberbullying Detection Network using Swin Transformer
Imran, A., & Li, J. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.105172 · Cited by 1 · see citing works
- Authors
- Pericherla, S., & Ilavarasan, E.
- Affiliations
- Puducherry Technological University (IN)
- Venue
- International Journal of Intelligent Unmanned Systems
- Publisher
- Emerald
- Published
- 2021-05-28
- Volume
- 12
- Issue
- 1
- Pages
- 154-166
- ISSN
- 2049-6427 (ISSN-L)
- OpenAlex
- W3170952411
- Cited by
- 23
- References
- 25
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 7 · 2023: 7 · 2022: 3
- FWCI
- 1.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Intelligent Unmanned Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- cyberbullying detection, transformer models, word embeddings, RoBERTa, LightGBM, social media comments, text classification, online harassment
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