Cyberbullying Identification System Based Deep Learning Algorithms
Open accessAbstract
Cyberbullying is characterized by deliberate and sustained peer aggression, as well as a power differential between the victim and the perpetrators or abusers. Cyberbullying can have a variety of consequences for victims, including mental health problems, poor academic performance, a tendency to drop out of work, and even suicidal thoughts. The main objective of this study was to develop a cyberbullying detection system (CDS) to uncover hateful and abusive behaviour on social media platforms. Two experiments were carried out to train and test the proposed system with binary and multiclass cyberbullying classification datasets. Hybrid deep learning architecture consisting of convolutional neural networks integrated with bidirectional long short-term memory networks (CNN-BiLSTM) and single BiLSTM models were compared in terms of their ability to classify social media posts into several bullying types related to gender, religion, ethnicity, age, aggression, and non-cyberbullying. Both classifiers showed promising performance in the binary classification dataset (aggressive or non-aggressive bullying), with a detection accuracy of 94%. For the multiclass dataset, BiLSTM outperformed the combined CNN-BiLSTM classifier, achieving an accuracy of 99%. A comparison of our method to the existing method on the multiclass classification dataset revealed that our method performed better in detecting online bullying.
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This limitation stems from the rapid generation of various forms of cyberbullying instances on social media platforms.
“This limitation stems from the rapid generation of various forms of cyberbullying instances on social media platforms [161].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [161]
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1 retracted
Alsubari, S. N., Deshmukh, S. N., Al-Adhaileh, M. H., Alsaade, F. W., & Aldhyani, T. H. H. (2021) · Applied Bionics and Biomechanics · doi:10.1155/2021/5522574 · Cited by 60 · see citing works
Self-citationRetractedEfficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568 · see citing works
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533 · see citing works
A Convolutional Neural Network for Modelling Sentences
Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1062 · Cited by 3,601 · see citing works
Cyberbullying: Another main type of bullying?
SLONJE, R., & SMITH, P. K. (2008) · Scandinavian Journal of Psychology · doi:10.1111/j.1467-9450.2007.00611.x · Cited by 1,844 · see citing works
Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network
Zhang, Z., Robinson, D., & Tepper, J. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93417-4_48 · Cited by 653 · see citing works
Using Machine Learning to Detect Cyberbullying
Reynolds, K., Kontostathis, A., & Edwards, L. (2011) · doi:10.1109/icmla.2011.152 · Cited by 462 · see citing works
A Convolutional Neural Network for Modelling SentencesPreprint
Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1404.2188 · Cited by 433 · see citing works
Automatic cyberbullying detection: A systematic review
Rosa, H., Pereira, N., Ribeiro, R., Ferreira, P. C., Carvalho, J. P., Oliveira, S., Coheur, L., Paulino, P., Veiga Simão, A. M., & Trancoso, I. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.12.021 · Cited by 307 · see citing works
Exploring Hate Speech Detection in Multimodal Publications
Gómez, R., Gibert, J., Gómez, L., & Karatzas, D. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093414 · Cited by 265 · see citing works
Improved cyberbullying detection using gender information
Dadvar, M., de Jong, F. M. G., Ordelman, R., & Trieschnigg, D. (2012) · University of Twente Research Information · Cited by 190 · see citing works
Englander, E., Donnerstein, E., Kowalski, R., Lin, C. A., & Parti, K. (2017) · Pediatrics · doi:10.1542/peds.2016-1758u · Cited by 189 · see citing works
Aggression detection through deep neural model on Twitter
Sadiq, S., Mehmood, A., Ullah, S., Ahmad, M., Choi, G. S., & On, B.-W. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.07.050 · Cited by 156 · see citing works
Chavan, V. S., & Shylaja, S. S. (2015) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2015.7275970 · Cited by 153 · see citing works
Aldhyani, T. H. H., Alsubari, S. N., Alshebami, A. S., Alkahtani, H. S., & Ahmed, Z. A. T. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph191912635 · Cited by 150 · see citing works
Self-citationSOSNet: A Graph Convolutional Network Approach to Fine-Grained Cyberbullying Detection
Wang, J., Fu, K., & Lu, C. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378065 · Cited by 139 · see citing works
Cyberbullying detection: Utilizing social media features
Bozyiğit, A., Utku, S., & Nasibov, E. N. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115001 · Cited by 131 · see citing works
Kontostathis, A., Reynolds, K., Garron, A., & Edwards, L. (2013) · ACM Web Science Conference (WebSci) · doi:10.1145/2464464.2464499 · Cited by 119 · 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
Detecting and visualizing hate speech in social media: A cyber Watchdog for surveillance
Modha, S., Majumder, P., Mandl, T., & Mandalia, C. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113725 · Cited by 109 · see citing works
Prediction of cyberbullying incidents in a media-based social network
Hosseinmardi, H., Rafiq, R. I., Han, R. Y., Lv, Q., & Mishra, S. (2016) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2016.7752233 · Cited by 109 · see citing works
Identification and characterization of cyberbullying dynamics in an online social network
Squicciarini, A., Rajtmajer, S. M., Liu, Y., & Griffin, C. H. (2015) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/2808797.2809398 · Cited by 94 · see citing works
Alzahrani, M. E., Aldhyani, T. H. H., Alsubari, S. N., Althobaiti, M. M., & Fahad, A. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/3840071 · Cited by 88 · see citing works
Self-citationDetection of cyberbullying on social media messages in Turkish
Özel, S. A., Eşsiz, E. S., Akdemir, S., & Aksu, H. (2017) · doi:10.1109/ubmk.2017.8093411 · Cited by 87 · 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
Towards understanding cyberbullying behavior in a semi-anonymous social network
Hosseinmardi, H., Ghasemian, A., Han, R. Y., Lv, Q., & Mishra, S. (2014) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2014.6921591 · Cited by 81 · see citing works
Towards Cyberbullying-free social media in smart cities: a unified multi-modal approach
Kumari, K., Singh, J. P., Dwivedi, Y. K., & Rana, N. P. (2020) · Soft Computing · doi:10.1007/s00500-019-04550-x · Cited by 81 · see citing works
Multilingual Cyberbullying Detection System
Pawar, R., & Raje, R. R. (2019) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit.2019.8833846 · Cited by 74 · see citing works
Kumari, K., Singh, J. P., Dwivedi, Y. K., & Rana, N. P. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.01.014 · Cited by 73 · see citing works
Chaffey, D., & Smith, P. (2022) · Routledge eBooks · doi:10.4324/9781003009498-6 · Cited by 66 · see citing works
Dessı̀, D., Recupero, D. R., & Sack, H. (2021) · Electronics · doi:10.3390/electronics10070779 · Cited by 50 · see citing works
Talpur, B. A., & O’Sullivan, D. (2020) · Informatics · doi:10.3390/informatics7040052 · Cited by 42 · see citing works
Cyberbullying Detection Using Deep Learning and Word Embeddings
Al-Hashedi, M., Soon, L.-K., & Goh, H.-N. (2019) · International Conference on Computational Intelligence and Intelligent Systems (CIIS) · doi:10.1145/3372422.3373592 · Cited by 34 · see citing works
Cyberbullying Detection by Using Artificial Neural Network Models
Bozyiğit, A., Utku, S., & Nasibov, E. N. (2019) · International Conference on Computer Science and Engineering (UBMK) · doi:10.1109/ubmk.2019.8907118 · Cited by 30 · see citing works
AUTOMATIC DETECTION OF CYBERBULLYING IN FORMSPRING.ME, MYSPACE AND YOUTUBE SOCIAL NETWORKS
Acı, Ç. İ., Çürük, E., & Eşsiz, E. S. (2019) · Turkish Journal of Engineering · doi:10.31127/tuje.554417 · Cited by 21 · see citing works
Prediction Using LSTM Networks
Arshi, S., Li, Z., & Strachan, R. (2019) · doi:10.1109/ijcnn.2019.8852206 · Cited by 11 · see citing works
Counselors and Cyberbullying: Guidelines for Prevention, Intervention, and Counseling
Johnson, L. D. (2011) · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- Yin, D.; Xue, Z.; Hong, L.; Davison, B.D.; Kontostathis, A.; Edwards, L. Detection of harassment on web 2.0. In Proceedings of the Content Analysis in the WEB, Madrid, Spain, 21 April 2009; pp. 1-7.
- Dinakar, K.; Reichart, R.; Lieberman, H. Modeling the detection of textual cyberbullying. In Proceedings of Proceedings of the International AAAI Conference on Web and Social Media, Atlanta, GA, USA, 6-9 June 2022.
- Ptaszynski, M.; Masui, F.; Kimura, Y.; Rzepka, R.; Araki, K. Extracting patterns of harmful expressions for cyberbullying detection. In Proceedings of the 7th Language & Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics (LTC'15), The First Workshop on Processing Emotions, Decisions and Opinions, Pozna ń, Poland, 27-29 November 2015; pp. 370-375.
- Hani, J.; Nashaat, M.; Ahmed, M.; Emad, Z.; Amer, E. Ammar Mohammed. Social media cyberbullying detection using machine learning. Int. J. Adv. Comput. Sci. Appl. 2019, 10, 5.
Cited by
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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
A Hybrid KAN-BiLSTM Transformer with Multi-Domain Dynamic Attention Model for Cybersecurity
Витальевич, Ч. А., Pleshakova, E., & Gataullin, S. T. (2025) · Technologies · doi:10.3390/technologies13060223 · Cited by 29 · see citing works
Cyberbullying detection and machine learning: a systematic literature review
Balakrisnan, V., & Kaity, M. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10553-w · Cited by 23 · see citing works
Deep Learning Algorithms for Cyber-Bulling Detection in Social Media Platforms
Obaida, M. H., Elkaffas, S. M., & Guirguis, S. K. (2024) · IEEE Access · doi:10.1109/access.2024.3406595 · Cited by 22 · see citing works
A hybrid deep learning approach for Assamese toxic comment detection in social media
Neog, M., & Baruah, N. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.04.218 · Cited by 17 · see citing works
Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms
Philipo, A. G., Sarwatt, D. S., Ding, J., Daneshmand, M., & Ning, H. (2026) · ACM Computing Surveys · doi:10.1145/3785654 · Cited by 14 · see citing works
Nasution, M. A. S., & Setiawan, E. B. (2023) · Revue d intelligence artificielle · doi:10.18280/ria.370413 · Cited by 12 · see citing works
Subhashree, K., & Kumar, S. M. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127555 · Cited by 11 · see citing works
Cyberbullying detection based on the fusion of DistilBERT and SIMHASH Technique
Kumar, C., Kumar, K. A., Gupta, S. K., & Sardar, T. H. (2024) · International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA) · doi:10.1109/aimla59606.2024.10531427 · Cited by 9 · see citing works
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Gupta, R., Singh, A. K., Utkarsh, Mittal, P., & Radhika (2024) · IEEE Delhi Section Conference (DELCON) · doi:10.1109/delcon64804.2024.10866680 · Cited by 8 · see citing works
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Azar, A. T., Noori, H. M., Mahlous, A. R., Alkhayyat, A. H., & Ibraheem, I. K. (2024) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.8314 · Cited by 6 · see citing works
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Haroon-Sulyman, S. O., Taiye, M. A., Kamaruddin, S. S., & Ahmad, F. K. (2024) · Communications in computer and information science · doi:10.1007/978-981-99-9589-9_13 · Cited by 4 · see citing works
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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
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Alsaade, F. W., & Al-zahrani, M. S. (2024) · Demonstratio Mathematica · doi:10.1515/dema-2023-0118 · Cited by 2 · see citing works
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Parikh, R., & Dalvi, A. (2024) · Springer eBooks · doi:10.1007/978-981-99-6984-5_6 · Cited by 2 · see citing works
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Segura-Navarrete, A., Martínez-Araneda, C., Labrín, S. M., Gómez, P., Vidal‐Castro, C., Espinoza-Silva, S., & Gutiérrez-Bunster, T. (2026) · IEEE Access · doi:10.1109/access.2026.3661575 · Cited by 1 · see citing works
Chow, D. V., Natania, F., Nathanael, O. T., Setiawan, K. E., & Hasani, M. F. (2023) · International Conference on Cybernetics and Intelligent System (ICORIS) · doi:10.1109/icoris60118.2023.10352294 · Cited by 1 · see citing works
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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
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Zedan, M. J. M., Saleh, A., Harun, H. H., & Zulkifley, M. A. (2024) · International Engineering Conference on Sustainable Technology and Development (IEC) · doi:10.1109/iec61018.2024.11064257
Vijayakumar, P. J., Das, M. S., Bansod, P. J., Kumar, S., Akshaya, R., & Praveena, S. (2025) · Global Conference in Emerging Technology (GINOTECH) · doi:10.1109/ginotech63460.2025.11076700
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Ghanem, D., Mansour, D., AlSayeh, R., Amari, Z., Hamzah, A., & Maabreh, M. (2025) · International Conference on Cybersecurity and AI-Based Systems (Cyber-AI) · doi:10.1109/cyber-ai66431.2025.11233349
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Kalyani, N. (2025) · International Journal of Innovative Research in Information Security · doi:10.26562/ijiris.2025.v1110.11
Scalable Deep Learning Frameworks for Cyberbullying Detection in Social Media
Ghafoor, M. I., Bhatti, U. A., Uglu, V. S. O., Xu, B., Noor, S., & Akram, S. (2025) · International Conference on Open Source Systems and Technologies (ICOSST) · doi:10.1109/icosst69113.2025.11315386
Han, M., Fuertes, W., Benavides, E., & Cruz, H. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-10947-7_23
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Selvanambi, R. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6239899
Sathea Sree, S., & Nalini Joseph, L. (2026) · International Research Journal of Multidisciplinary Technovation · doi:10.54392/irjmt26216
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Tjahjana, J., Sugihartono, Y., Achmad, S., & Suhartono, D. (2025) · International Conference on Artificial Intelligence, Blockchain, Cloud Computing, and Data Analytics (ICoABCD) · doi:10.1109/icoabcd67551.2025.11470717
Sathea Sree, S., & Nalini Joseph, L. (2026) · International Research Journal of Multidisciplinary Technovation · doi:10.54392/irjmt26315
Binjhade, S., Khan, S., Tiwari, D., & Patidar, K. (2026) · Information systems engineering and management · doi:10.1007/978-3-032-23544-2_19
Abid, E., Rafaqat, N., & Wasim, M. (2026) · International Conference on Information Science and Communication Technology (ICISCT) · doi:10.1109/icisct67740.2026.11576438
Abarna, S., & Immanuel, S. J. (2026) · Journal of Intelligent & Fuzzy Systems · doi:10.1177/18758967261478011
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- Authors
- Aldhyani, T. H. H., Al-Adhaileh, M. H., & Alsubari, S. N.
- Affiliations
- King Faisal University (SA)
- Dr. Babasaheb Ambedkar Marathwada University (IN)
- Corresponding author
- Aldhyani, Theyazn H. H.
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2022-10-12
- Volume
- 11
- Issue
- 20
- Pages
- 3273
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W4304688661
- Cited by
- 55
- References
- 37
- Citations by year
- 2026: 12 · 2025: 16 · 2024: 20 · 2023: 7
- FWCI
- 3.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Deanship of Scientific Research at King Faisal University (NA000234)
- 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 media, CNN-BiLSTM, bidirectional LSTM, multi-class classification, binary classification, online aggression, hate speech
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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