Revolutionizing Cyber-Bullying Detection with the BullyNet Deep Learning Framework

Sathea Sree S; Nalini Joseph L

doi:10.54392/irjmt2524

Authors

Sathea Sree S Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India. Author https://orcid.org/0000-0001-6974-1403
Nalini Joseph L Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India. Author

DOI:

https://doi.org/10.54392/irjmt2524

Keywords:

Cyberspace, Bullying, Online Content, Detection, Accuracy, Preprocessing, Training, Optimization, Social Networking

Abstract

Cyber-bullying has emerged as one of the most common social problems in online social networks, where advanced techniques of detection are required against its overwhelming growth. As the fastest-moving entity, the digital communication mechanism still needs to develop more effective ways to locate and diminish Cyber-bullying cases, which is a crucial area of research in developing more sophisticated and accurate detection systems. This study is new as it utilizes novel technology called "BullyNet," the state–of–the–art deep learning model, to address the Cyber-bullying phenomenon uniquely. Our efforts in this study are to design and deploy BullyNet, a novel deep-learning model that combines cutting-edge feature extraction and representation techniques to distinguish Cyber-bullying activities from other types of online behavior appropriately. The model is designed to detect minutiae linguistic and contextual cues associated with online harassment, using a multi-layered approach to fine-tune and optimize its performance, which enables it to reduce false-harassment detections. The effectiveness of BullyNet was validated and verified through extensive testing and validation on a popularly diverse dataset drawn from various social networks online. The model that was developed exhibited a precipitous accuracy of up to 95% and displayed its advanced capability for detecting tricky bullying patterns while at the same time reducing deficient levels of false positives. Besides the described enhancement in cyber-harassment detection, this theme unveils an opportunity for a more secure and nurturing online social environment.

References

N. Amalina, M. Chinniah, A.A. Othman, P. Shamala, Cyberbullying: A Systematic Literature Review on the Definitional Criteria. International Journal of Academic Research in Business and Social Sciences, 12(3), (2022) 265-280. http://dx.doi.org/10.6007/IJARBSS/v12-i3/12042

M.E. Kula, Cyberbullying: A Literature Review on Cross-Cultural Research in the Last Quarter. Handbook of Research on Digital Violence and Discrimination Studies, (2022) 610-630. https://doi.org/10.4018/978-1-7998-9187-1.ch027

S. Grover, V.V. Raju, Cyberbullying: A Narrative Review. Journal of Mental Health and Human Behaviour, 28(1), (2023) 17-26. https://doi.org/10.4103/jmhhb.jmhhb_47_22

D. Aizenkot, The predictability of routine activity theory for cyberbullying victimization among children and youth: Risk and protective factors. Journal of interpersonal violence, 37(13-14), (2022) NP11857-NP11882. https://doi.org/10.1177/0886260521997433

S.M. Fati, A. Muneer, A. Alwadain, A.O. Balogun, Cyberbullying detection on twitter using deep learning-based attention mechanisms and continuous Bag of words feature extraction. Mathematics, 11(16), (2023) 3567. https://doi.org/10.3390/math11163567

L. Hebert, L. Golab, R. Cohen, (2022) Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Context. In 2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), IEEE, Canada. https://doi.org/10.1109/WI-IAT55865.2022.00012

M. Subramanian, V.E. Sathiskumar, G. Deepalakshmi, J. Cho, G. Manikandan, A survey on hate speech detection and sentiment analysis using machine learning and deep learning models. Alexandria Engineering Journal, 80, (2023) 110-121. https://doi.org/10.1016/j.aej.2023.08.038

Q. Huang, V.K. Singh, P.K. Atrey, Cyber bullying detection using social and textual analysis. In Proceedings of the 3rd International Workshop on Socially-aware Multimedia, (2014) 3-6. https://doi.org/10.1145/2661126.2661133

M. Di Capua, E. Di Nardo, A. Petrosino, (2016) Unsupervised cyber bullying detection in social networks. In 2016 23rd International conference on pattern recognition (ICPR), IEEE, Mexico. https://doi.org/10.1109/ICPR.2016.7899672

N.A. Azeez, S.O. Idiakose, C.J. Onyema, C. Van Der Vyver, Cyberbullying detection in social networks: Artificial intelligence approach. Journal of Cyber Security and Mobility, (2021) 745-774. https://doi.org/10.13052/jcsm2245-1439.1046

Desai, S. Kalaskar, O. Kumbhar, R. Dhumal, Cyber bullying detection on social media using machine learning. In ITM Web of Conferences, 40, (2021) 03038. https://doi.org/10.1051/itmconf/20214003038

N. Balaji, B.H. Karthik Pai, K. Manjunath, B. Venkatesh, N. Bhavatarini, B.K. Sreenidhi, Cyberbullying in online/e-learning platforms based on social networks. In Intelligent Sustainable Systems: Selected Papers of WorldS4 2021, 2, (2022) 227-240. https://doi.org/10.1007/978-981-16-6369-7_20

Bansal, A. Baliyan, A. Yadav, A. Kamlesh, H.K. Baranwal, Cyberbullying Detection on Social Networks Using Machine Learning Approaches. International Research Journal of Engineering and Technology (IRJET), 9, (2022). https://doi.org/10.1007/978-981-16-6369-7_20

A.A. Albraikan, S.H. Hassine, S.M. Fati, F.N. Al-Wesabi, A.M. Hilal, A. Motwakel Hilal, A. Motwakel, M.A. Hamza, M. Al Duhayyim, Optimal deep learningbased cyberattack detection and classification technique on social networks. Computers, Materials & Continua, 72(1), (2022) 907-923. https://doi.org/10.32604/cmc.2022.024488

Kumar, N. Sachdeva, A Bi-GRU with attention and CapsNet hybrid model for cyberbullying detection on social media. World Wide Web, 25(4), (2022) 1537-1550. https://doi.org/10.1007/s11280-021-00920-4

B.A.H. Murshed, J. Abawajy, S. Mallappa, M.A.N. Saif, H.D.E. Al-Ariki, DEA-RNN: A hybrid deep learning approach for cyberbullying detection in Twitter social media platform. IEEE Access, 10, (2022) 25857-25871. https://doi.org/10.1109/ACCESS.2022.3153675

M. López-Vizcaíno, F.J. Nóvoa, T. Artieres, F. Cacheda, Site agnostic approach to early detection of cyberbullying on social media networks. Sensors, 23(10), (2023) 4788. https://doi.org/10.3390/s23104788

N. Ejaz, S. Choudhury, F. Razi, A comprehensive dataset for automated cyberbullying detection. Mendeley Data, 2, (2024) 2024.

A. Willisch, A. Wolgast, M. Donat, (2022) Dataset for: Cyber Bullying among University Students [Data set]. Psych Archives. https://doi.org/10.23668/psycharchives.5597

B. Alhijawi, A. Awajan, Genetic algorithms: Theory, genetic operators, solutions, and applications. Evolutionary Intelligence, 17(3), (2024) 1245-1256. https://doi.org/10.1007/s12065-023-00822-6

W. Wang, T. Chen, W. Wu, (2023). Reinforcement Learning for Combating Cyberbullying in Online Social Networks. Combinatorial Optimization and Applications, Springer Nature Switzerland https://doi.org/10.1007/978-3-031-49614-1_36

R.T. Icarte, T.Q. Klassen, R. Valenzano, S.A. McIlraith, Reward machines: Exploiting reward function structure in reinforcement learning. Journal of Artificial Intelligence Research, 73, (2022) 173-208. https://doi.org/10.1613/jair.1.12440

L.G.C. Evangelista, R. Giusti, (2022) Short-and-Long-Term Impact of Initialization Functions in NeuroEvolution. Brazilian Conference on Intelligent Systems, Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-21686-2_21

B.S. Panigrahi, R.K. Kanna, P.P. Das, S.K. Sahoo, T. Dutta, Machine Learning Based Intelligent Management System for Energy Storage Using Computing Application. EAI Endorsed Transactions on Energy Web, 11, (2024). https://doi.org/10.4108/ew.6272

Y. Wang, Z. Xiao, G. Cao, A convolutional neural network method based on Adam optimizer with power-exponential learning rate for bearing fault diagnosis. Journal of Vibroengineering, 24(4), (2022) 666-678. https://doi.org/10.21595/jve.2022.22271

Z. Khan, R.K. Kanna, K. Parthasarathy, S. Vijay raj, R. Chandrasekaran, S. Jawla, Intelligent computational ensemble model for predicting cerebral aneurysm using the concept of region localization in multi-section CT angiography. International Journal of Information Technology, (2025) 1-7. https://doi.org/10.1007/s41870-024-02292-0

R.K. Kanna, A.O. Salau, New Cognitive Computational Strategy for Optimizing Brain Tumour Classification using Magnetic Resonance Imaging Data. Intelligence-Based Medicine, (2025) 100215. https://doi.org/10.1016/j.ibmed.2025.100215

S. Umamaheswaran, R. John, S. Nagarajan, K.M. Karthick Raghunath, Arvind, K. S. (2022). Predictive Assessment of Fetus Features Using Scanned Image Segmentation Techniques and Deep Learning Strategy. International Journal of e-Collaboration (IJeC), 18(3), 1-13. https://doi.org/10.4018/IJeC.307130