Cyberbullying Detection in Social Networks: A Comparison Between Machine Learning and Transfer Learning Approaches
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Information and Communication Technologies fueled social networking and facilitated communication. However, cyberbullying on the platform had detrimental ramifications. The user-dependent mechanisms like reporting, blocking, and removing bullying posts online is manual and ineffective. Bag-of-words text representation without metadata limited cyberbullying post text classification. This research developed an automatic system for cyberbullying detection with two approaches: Conventional Machine Learning and Transfer Learning. This research adopted AMiCA data encompassing significant amount of cyberbullying context and structured annotation process. Textual, sentiment and emotional, static and contextual word embeddings, psycholinguistics, term lists, and toxicity features were used in the conventional Machine Learning approach. This study was the first to use toxicity features to detect cyberbullying. This study is also the first to use the latest psycholinguistics features from the Linguistic Inquiry and Word (LIWC) 2022 tool, as well as Empath’s lexicon, to detect cyberbullying. The contextual embeddings of ggeluBert, tnBert, and DistilBert have alike performance, however DistilBert embeddings were elected for higher F-measure. Textual features, DistilBert embeddings, and toxicity features that struck new benchmark were the top three unique features when fed individually. The model’s performance was boosted to F-measure of 64.8% after feeding with a combination of textual, sentiment, DistilBert embeddings, psycholinguistics, and toxicity features to the Logistic Regression model that outperforms Linear SVC with faster training time and efficient handling of high-dimensionality features. Transfer Learning approach was by fine-tuning optimized version Pre-trained Language Models namely, DistilBert, DistilRoBerta, and Electra-small which were found to have speedier training computation than their base form. The fine-tuned DistilBert resulted with the highest F-measure of 72.42%, surpassing CML. Our research concluded that Transfer Learning was the best for uplifted performance and lesser effort as feature engineering and resampling was omitted.
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The scarcity of quality cyberbullying datasets in languages with limited resources obstructs the development of effective detection models for local contexts.
“Moreover, the scarcity of quality cyberbullying datasets in languages with limited resources obstructs the development of effective detection models for local contexts [155].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [155]
Utilizing transfer learning and fine-tuning techniques demands extensive computational resources and time, along with abundant data to enhance the detection capabilities of models.
“Utilizing transfer learning and fine-tuning techniques demands extensive computational resources and time, along with abundant data to enhance the detection capabilities of models [155].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [155]
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Javed, N., Ahmed, T., & Faisal, M. (2024) · International Journal of Bullying Prevention · doi:10.1007/s42380-024-00282-1 · Cited by 1 · see citing works
Identifying Cyberbullying Roles in Social Media
Sandoval, M., Abuhamad, M., Furman, P., Nazari, M., Hall, D. L., & Silva, Y. N. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-78548-1_26
Victimizing Cyberbullying Mental Illness Through Social Media
Choesang, T., & Gokulapriya, R. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-97-7717-4_20
Machine Learning Approaches To Detect And Mitigate Cyberbullying In Online Communities
Smitha, P. B., & Kavipriya, P. (2024) · International Conference on Integration of Emerging Technologies for the Digital World (ICIETDW) · doi:10.1109/icietdw61607.2024.10939760
An Efficient Deep Learning‐Based Framework for Predicting Cyber Violence in Social Networks
Fathabad, Y. F., Balafar, M. A., Oskouei, A. G., & Koohi, K. (2025) · Complexity · doi:10.1155/cplx/2750326
Gupta, S., Ray, K., & Kanwer, B. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-0185-1_10
Cyberbullying Detection Using Fine-Tuned BERT
Bhagyalekshmi, G. S., Harikrishnan, D., & Salaji, S. (2025) · International Conference on Trends in Engineering Systems and Technologies (ICTEST) · doi:10.1109/ictest64710.2025.11042412
Enhance Social Network Bullying Detection using Transfer Learning with Random Forest Classifier
Prasomphan, S. (2025) · International Conference on Computing and Artificial Intelligence (ICCAI) · doi:10.1109/iccai66501.2025.00120
Cyberbullying Detection on Twitter: A Comparison of One-Step vs. Two-Step Classification Approaches
Halim, D. E. S., Tandoko, F. A., Wulandhari, L. A., & Nabiilah, G. Z. (2025) · IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) · doi:10.1109/iaict65714.2025.11101479
An Interpretable Data-Driven Approach for Modeling Toxic Users via Feature Extraction
Pollacci, L., Gneri, J., & Guidotti, R. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-08327-2_9
Kuri, M. M., & Pathak, G. R. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-06691-6_40
A Deep Learning-Based Framework for Detecting Cyberbullying on Social Media Platforms
Manjushree, H. C., H, S. G. G., Ninawe, S. S., & Harini, L. (2025) · International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS) · doi:10.1109/iccams65118.2025.11234157
Adaptive Real-Time Context-Aware Cyberbullying Detection and Intervention Framework
Bhole, K., & Ugale, T. (2025) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea65967.2025.11284262
Ramana, C. V., Sumathy, V., Vullam, N., Prasanthi, G., Lakshmanarao, A., & Rao, P. V. (2025) · International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS) · doi:10.1109/icicnis66685.2025.11315743
Chu, C. C. F., Tong, C. C. H., Chiu, C. H., & Lam, S. C. (2026) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2026.200652
A REVIEW OF CYBERBULLYING DETECTION ON SOCIAL MEDIA
Ali, M. A., Sulaiman, N. S., & Apandi, Z. F. M. (2026) · International Journal of Law Government and Communication · doi:10.35631/ijlgc.1143028
Sathea Sree, S., & Nalini Joseph, L. (2026) · International Research Journal of Multidisciplinary Technovation · doi:10.54392/irjmt26315
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Loshini, S., Lithika, S., Karthika, N., Geetha, R., & Belshia Jebamalar, G. (2026) · International Conference on Intelligent Computing, Networks, and Security (IC-ICNS) · doi:10.1109/ic-icns68863.2026.11537491
Ashwin Shenoy, M., & Tunga, N. B. (2026) · International Conference on Artificial Intelligence and Data Engineering (AIDE) · doi:10.1109/aide69088.2026.11544495
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Kuri, M. M., & Pathak, G. R. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-27163-1_24
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R, A. S. A., Angel Felciya, I., Kanishka, B., Harini, S., Paul, J., & Jones, A. (2026) · International Conference on Data Science and Business Systems (ICDSBS) · doi:10.1109/icdsbs69077.2026.11634812
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Teng, T. H., Varathan, K. D., & Crestani, F. (2026) · Heliyon · doi:10.1016/j.heliyon.2026.e45491
Self-citation
- Authors
- Teng, T. H., & Varathan, K. D.
- Affiliations
- University of Malaya (MY)
- Venue
- IEEE Access
- Published
- 2023
- Volume
- 11
- Pages
- 55533-55560
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4382311624
- Cited by
- 77
- References
- 144
- Citations by year
- 2026: 23 · 2025: 30 · 2024: 19 · 2023: 5
- FWCI
- 7.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Access (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Impact Oriented Interdisciplinary Research Grant Universiti Malaya (IIRG001A-19SAH)
- 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, social networks, transfer learning, DistilBERT, psycholinguistic features, pre-trained language models, text classification, feature engineering
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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