Cyber Bullying Detection on Social Media Network
Open accessAbstract
Abstract: Cyberbullying has grown more prevalent on social networking sites. Cyberbullying has resulted in a significant increase in mental health issues, particularly among the younger population. Self-esteem and mental health difficulties will affect a whole generation of young adults unless action is made to combat cyberbullying. Many classical machine learning methods have already been used for the automated identification of cyberbullying on social media. Since the advent of social media platforms about 20 years ago, there haven't been many effective ways to stop social bullying, and it has recently grown to be one of the most concerning problems. In this project, we create an AI-based system for detecting cyberbullying and investigate ways to identify fraudulent profiles and abusive speech on social media while separating them from ordinary vulgarity. Applying supervised classification techniques to a manually annotated open-source dataset, we seek to create lexical baselines for this job.
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It is difficult to develop cyberbullying automatic detection systems which are multipurpose to adapt to different digital platforms.
“It is difficult to develop cyberbullying automatic detection systems which are multipurpose to adapt to different digital platforms [9].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [9]
Twitter provides a vast amount of data due to its large user base, resulting in a significant number of active users worldwide.
“Moreover, Twitter provides a vast amount of data due to its large user base, resulting in a significant number of active users worldwide [9,131,132].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [9]
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Flagged first, then by citations
Research works printed but not linked: 4
- Hii Lee Jia, Vazeerudeen Abdul Hameed, Muhammad Ehsan Rana, 'CyberSaver â€" A Machine Learning Approach to Detection of Cyber Bullying', 2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM)
- Amanpreet Singh, Maninder Kaur, "Content-based Cybercrime Detection: A Concise Review", International Journal of Innovative Technology and Exploring Engineering(IJITEE) ISSN: 2278-3075, Volume-8 Issue-8,pages 1193-1207, 2019
- R. Zhao, A. Zhou, K. Mao, Automatic detection of cyberbullying on social networks based on bullying features, in Proceedings of the 17th International Conference on Distributed Computing and Networking, 2016.
- Amanpreet Singh, Maninder Kaur, "Content-based Cybercrime Detection: A Concise Review", International Journal of Innovative Technology and Exploring Engineering(IJITEE) ISSN: 2278-3075, Volume-8 Issue-8,pages 1193-1207, 2019
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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
- Authors
- P, C., K, D., M, H., & J, K.
- Venue
- International Journal for Research in Applied Science and Engineering Technology
- Published
- 2023-04-30
- Volume
- 11
- Issue
- 4
- Pages
- 4182-4186
- ISSN
- 2321-9653 (ISSN-L)
- OpenAlex
- W4367600500
- Cited by
- 1
- References
- 16
- Citations by year
- 2025: 1
- FWCI
- 0.09
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- International Journal for Research in Applied Science and Engineering Technology (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- cyberbullying detection, social media, supervised classification, mental health, young adults
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