Citation :

I. Anand Raj, R. Vidya, A. Martin, "Optimizing Bullying News Detection on Social Media: An Ensemble Deep Learning Approach with Enhanced Optimization Algorithm," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 2, pp. 266-279, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I2P119

Abstract

Cyberbullying is a growing digital threat that exploits online platforms to harm individuals, which can take place on social media, messaging environments, gaming, and mobile phones. Cyberbullying can result in deep psychiatric and emotional illnesses for those affected. Henceforth, there is a crucial necessity to develop an automated model for cyberbullying detection. Detecting cyberbullying is very challenging, due to the occurrence of complete affective content, which is also frequently sarcastic, and multimodal, such as audio, text, and image. Presently, Deep Learning (DL) techniques have attained extraordinary achievements in numerous tasks and are employed for the detection of cyberbullying for multimodal data. This paper presents the Multi-Layer Perceptron and Feature Extraction for Detecting Bullying in Multimodal Content (MLPFE-DBMMC) technique. The MLPFE-DBMMC technique aims to develop an effective multimodal cyberbullying detection framework by leveraging audio, textual, and visual inputs to recognize and identify harmful behaviour. Initially, the preprocessing stage is performed in multimodal formats such as image, audio, and text. For image preprocessing, the anisotropic diffusion method is employed for noise removal. The stationary wavelet transform-based noise removal is applied in the audio preprocessing. Moreover, the text preprocessing stage involves various levels such as lower case, tokenization, removal of stopwords, and stemming. For the feature extraction process, the MLPFE-DBMMC model implements the Contrastive Language-Image Pre-Training (CLIP) method for image, VGGish-based audio, and the generative pre-training-2 (GPT-2) technique is employed for text. After feature extraction from the multimodal data, output features of size (3672, 512) for images, (3672, 128) for audio, and (3672, 768) for text are obtained. Three multimodal features are fused via concatenation for final classification. The MLPFE-DBMMC model implements the correlation alignment loss (CORAL) multi-layer perceptron technique for the multimodal cyberbullying detection process. Finally, the improved artificial rabbit’s optimization (IARO)-based hyperparameter tuning is performed to enhance the detection outcomes. A wide range of experiments of the MLPFE-DBMMC approach is performed under the MultiBully dataset. The comparison study of the MLPFE-DBMMC approach portrayed a superior accuracy value of 94.44% over existing techniques.

Keywords

Multimodal; Bullying Detection, Multi-Layer Perceptron, Improved Artificial Rabbits Optimization, Contrastive Language-Image Pre-Training.

References

[1]     Lu Cheng et al., “XBully: Cyberbullying Detection within a Multi-Modal Context, Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, Association for Computing Machinery, New York, United States, pp. 339-347, 2019.
[CrossRef] [Google Scholar] [Publisher Link]

[2]     Kaige Wang et al., “Multimodal Cyberbullying Detection on Social Networks,” 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, pp. 1-8, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[3]     Shuai Wang et al., “A Review of Multimodal-based Emotion Recognition Techniques for Cyberbullying Detection in Online Social Media Platforms,” Neural Computing and Applications, vol. 36, no. 35, pp.21923-21956, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[4]     Krishanu Maity et al., “A Multitask Framework for Sentiment, Emotion and Sarcasm Aware Cyberbullying Detection from Multi-Modal Code-Mixed Memes,” Proceedings of the 45^th International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, United States, pp. 1739-1749, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[5]     Hong Lin et al., “Special Issue on Deep Learning Methods for Cyberbullying Detection in Multimodal Social Data,” Multimedia Systems, vol. 28, no. 6, pp.1873-1875, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[6]     Mahmoud Ahmad Al-Khasawneh et al., “Toward Multimodal Approach for Identification and Detection of Cyberbullying in Social Networks,” IEEE Access, vol. 12, pp. 90158-90170, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[7]     Sayanta Paul, Sriparna Saha, and Mohammed Hasanuzzaman, “Identification of Cyberbullying: A Deep Learning based Multimodal Approach,” Multimedia Tools and Applications, vol. 81, no. 19, pp.26989-27008, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[8]     Tingting Li et al., “Integrating GIN-based Multimodal Feature Transformation and Multi-Feature Combination Voting for Irony-Aware Cyberbullying Detection,” Information Processing and Management, vol. 61, no. 3, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[9]     Md. Tofael Ahmed et al., “Multimodal Cyberbullying Meme Detection from Social Media using Deep Learning Approach,” International Journal of Computer Science and Information Technology, vol. 15, no. 4, pp. 27-37, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[10]  Subbaraju Pericherla, and Ilavarasan Egambaram, “Cyberbullying Detection on Multimodal Data using Pre-Trained Deep Learning Architectures,” Ingeniería Solidaria, vol. 17, no. 3, pp.1-20, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[11]  C. Valliyammai et al., “Cyberbullying Detection in Social Media with Multimodal Data using Transfer Learning,” 2024 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), Chennai, India, pp. 443-447, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[12]  Neha Minder Singh, and Sanjay Kumar Sharma, “Multimodal Cyberbullying Detection with Severity Analysis using Deep-Tensor Fusion Framework,” International Journal of Computer Network and Information Security (IJCNIS), vol. 17, no. 3, pp. 144-150, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[13]  Md. Anas Mondol et al., “AI-Powered Frameworks for the Detection and Prevention of Cyberbullying Across Social Media Ecosystems,” TechComp Innovations: Journal of Computer Science and Technology, vol. 2, no. 1, pp. 1-15, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[14]  Neha Minder Singh, and Sanjay Kumar Sharma, “An Efficient Automated Multimodal Cyberbullying Detection using Decision Fusion Classifier on Social Media Platforms,” Multimedia Tools and Applications, vol. 83, no. 7, pp. 20507-20535, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[15]  Jiani Wang et al., “Hierarchical Multi-Stage BERT Fusion Framework with Dual Attention for Enhanced Cyberbullying Detection in Social Media,” 2024 4^th International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC), Xiamen, China, pp. 86-89, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[16]  Sandip A. Kahate, and Atul D. Raut, “Design of a Deep Learning Model for Cyberbullying and Cyberstalking Attack Mitigation via Online Social Media Analysis,” 2023 4^th International Conference on Innovative Trends in Information Technology (ICITIIT), Kottayam, India, pp. 1-7, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[17]  Nagwan Abdel Samee et al., “Safeguarding Online Spaces: A Powerful Fusion of Federated Learning, Word Embeddings, and Emotional Features for Cyberbullying Detection,” IEEE Access, vol. 11, pp.124524-124541, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[18]  Syed Rizwana, Lenin Laitonjam, and Ranjita Das, “Adaptive Anisotropic Diffusion Filter in Unsharp Masking Scheme for Mammogram Enhancement using PLIP Operations,” Procedia Computer Science, vol. 258, pp. 3468-3479, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[19]  Vandana Akshath Raj, Subramanya G. Nayak, and Ananthakrishna Thalengala, “A Hybrid Framework for Muscle Artifact Removal in EEG: Combining Variational Mode Decomposition, Stationary Wavelet Transform, and Canonical Correlation Analysis,” Cogent Engineering, vol. 12, no. 1, pp. 1-19, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[20]  Mahazam Afrad et al., “Sentiment Analysis of Visitor Reviews on Baturaden Tourist Attraction using Machine Learning Methods,” Edu Komputika Journal, vol. 11, no. 1, pp. 57-64, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[21]  Xiang Li et al., “RS-CLIP: Zero Shot Remote Sensing Scene Classification via Contrastive Vision-Language Supervision,” International Journal of Applied Earth Observation and Geoinformation, vol. 124, pp. 1-12, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[22]  Komal Shahzad et al., “Enhancing Voice Spoofing Detection: A Hybrid Approach with VGGish-LSTM Model for Improved Security in Automatic Speaker Verification Systems,” IEEE Access, vol. 13, pp. 40682-40702, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[23]  Mariam Bangura et al., “Automatic Generation of German Drama Texts using Fine Tuned GPT-2 Models,” arXiv Preprint, pp. 1-18, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[24]  Wenzhi Cao, Vahid Mirjalili, and Sebastian Raschka, “Rank Consistent Ordinal Regression for Neural Networks with Application to Age Estimation,” Pattern Recognition Letters, vol. 140, pp. 325-331, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[25]  Quyet Nguyen Huu et al., “An Improved Artificial Rabbit Optimization for Structural Damage Identification,” Latin American Journal of Solids and Structures, vol. 21, no. 1, pp. 1-18, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[26]  Muhammad Shoib Amin et al., “Dual-Branch Neural Network for Bridging Semantic Gap in Harmful Meme Detection,” IEEE Access, vol. 13, pp. 125090-125100, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[27]  Prashant Kapil, and Asif Ekbal, “A Transformer based Multi Task Learning Approach to Multimodal Hate Speech Detection,” Natural Language Processing Journal, vol. 11, pp. 1-13, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[27] A.K. Indira Kumar et al., “Multi-Task Detection of Harmful Content in Code-Mixed Meme Captions using Large Language Models with Zero-Shot, Few-Shot, and Fine-Tuning Approaches,” Egyptian Informatics Journal, vol. 30, pp. 1-20, 2025.
[CrossRef] [Google Scholar] [Publisher Link]