Cyberbullying detection using deep transfer learning
Open accessAbstract
Social networking platforms like Facebook, Twitter, and others have numerous advantages, but they have many dark sides also. One of the issues on these social platforms is cyberbullying. The impact of cyberbullying is immeasurable on the life of victims as it’s very subjective to how the person would tackle this. The message may be a bully for victims, but it may be normal for others. The ambiguities in cyberbullying messages create a big challenge to find the bully content. Some research has been reported to address this issue with textual posts. However, image-based cyberbullying detection is received less attention. This research aims to develop a model that helps to prevent image-based cyberbullying issues on social platforms. The deep learning-based convolutional neural network is initially used for model development. Later, transfer learning models are utilized in this research. The experimental outcomes of various settings of the hyper-parameters confirmed that the transfer learning-based model is the better choice for this problem. The proposed model achieved a satisfactory accuracy of 89% for the best case, indicating that the system detects most cyberbullying posts.
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These complexities involve cultural nuances, unconventional use of linguistic resources, and the availability of native language input methods.
“These complexities involve cultural nuances, unconventional use of linguistic resources, and the availability of native language input methods [160].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [160]
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References 35 · Cited by 57
Research works printed but not linked: 4
- Cheng L, Li J, Silva YN, Hall DL, Liu H (2019) Xbully: Cyberbul- lying detection within a multi-modal context. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 339-347
- Singh VK, Ghosh S, Jose C (2017) Toward multimodal cyber- bullying detection. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pp. 2090-2099
- Al-Ajlan MA, Ykhlef M (2018) Deep learning algorithm for cyber- bullying detection. Int J Adv Comput Sci Appl 9(9):199-205
- Udendhran R, Balamurugan M (2021) Towards secure deep learn- ing architecture for smart farming-based applications. Complex Intell Syst 7(2):659-666
- Authors
- Roy, P. K., & Mali, F. U.
- Affiliations
- Indian Institute of Information Technology Surat (IN)
- Corresponding author
- Roy, Pradeep Kumar
- ORCID
- Roy, Pradeep Kumar
- Venue
- Complex & Intelligent Systems
- Published
- 2022-05-25
- Volume
- 8
- Issue
- 6
- Pages
- 5449-5467
- ISSN
- 2198-6053 (ISSN-L)
- 2199-4536
- OpenAlex
- W4281570478
- Cited by
- 57
- References
- 35
- Citations by year
- 2026: 11 · 2025: 18 · 2024: 12 · 2023: 15 · 2022: 1
- FWCI
- 3.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Complex & Intelligent Systems (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- cyberbullying detection, transfer learning, convolutional neural networks, social media, online harassment
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