AI Powered Anti-Cyber Bullying System using Machine Learning Algorithm of Multinomial Naive Bayes and Optimized Linear Support Vector Machine
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Abstract (arXiv)
"Unless and until our society recognizes cyber bullying for what it is, the suffering of thousands of silent victims will continue." ~ Anna Maria Chavez. There had been series of research on cyber bullying which are unable to provide reliable solution to cyber bullying. In this research work, we were able to provide a permanent solution to this by developing a model capable of detecting and intercepting bullying incoming and outgoing messages with 92% accuracy. We also developed a chatbot automation messaging system to test our model leading to the development of Artificial Intelligence powered anti-cyber bullying system using machine learning algorithm of Multinomial Naive Bayes (MNB) and optimized linear Support Vector Machine (SVM). Our model is able to detect and intercept bullying outgoing and incoming bullying messages and take immediate action.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 0 of its 16 references that are in the map. First posted on arXiv 25 Jul 2022.
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Citing papers per year: 2023: 2; 2024: 2; 2025: 1.
5 papers.
- 2023Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detectioncited by 4
- 2024An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Surveycited by 1
- 2024An investigation into the performances of the Current state-of-the-art Naive Bayes, Non-Bayesian and Deep Learning Based Classifier for Phishing Detection: A Surveycited by 1
- 2023Ambient Technology & Intelligencecited by 0
- 2025MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection and Classificationcited by 0
- 2025MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection and Classification19 Dec 2025
- 2024An investigation into the performances of the Current state-of-the-art Naive Bayes, Non-Bayesian and Deep Learning Based Classifier for Phishing Detection: A Survey24 Nov 2024
- 2024An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey26 Feb 2024
- 2023Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detection22 Aug 2023
- 2023Ambient Technology & Intelligence18 May 2023