Almomani, A, Gupta, B B, Nahar, Khalid, Alauthman, Mohammad, Al-Betar, Mohammed Azmi and Yaseen, Qussai (2024) Image cyberbullying detection and recognition using transfer deep machine learning. International Journal of Cognitive Computing in Engineering, 5. pp. 14-26. ISSN 2666-3074
pii/S2666307423000360 - Published Version
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Abstract
Cyberbullying detection on social media platforms is increasingly important, necessitating robust computational methods. Current approaches, while promising, have not fully leveraged the combined strengths of deep learning and traditional machine learning for enhanced performance. Moreover, online content complexity requires models that can capture nuanced contexts beyond text, which many current methods lack. This research proposes a novel hybrid approach using deep learning models as feature extractors and machine learning classifiers to improve cyberbullying detection. Extracting features using pre-trained deep learning models like InceptionV3, ResNet50, and VGG16, then feeding them into classifiers like Logistic Regression and Support Vector Machines, enhances understanding of the complex contexts where cyberbullying occurs. Experiments on an image dataset showed that combining deep learning and machine learning achieved higher accuracy than using either approach alone. This novel framework bridges the gap in existing literature and contributes to broader efforts to combat cyberbullying through more nuanced, context-aware detection methods. The hybrid technique demonstrates the potential of blending deep learning's representation learning strengths with machine learning's sample efficiency and interpretability.
Affiliation: | Skyline University College |
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SUC Author(s): | Almomani, A ORCID: https://orcid.org/0000-0002-8808-6114 and Gupta, B B |
All Author(s): | Almomani, A, Gupta, B B, Nahar, Khalid, Alauthman, Mohammad, Al-Betar, Mohammed Azmi and Yaseen, Qussai |
Item Type: | Article |
Uncontrolled Keywords: | Social media; Cyberbullying; CNN; Transfer learning; Machine learning |
Subjects: | B Information Technology > BL Machine Learning B Information Technology > BR Deep Learning |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Mosys Team |
Date Deposited: | 29 Jan 2024 11:34 |
Last Modified: | 29 Jan 2024 11:34 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/826 |
Publisher URL: | https://doi.org/10.1016/j.ijcce.2023.11.002 |
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