Cyber Harassment Prediction In Social Media Using Word CNN

Authors

  • Mr.Anil Kumar Sir Associate professor, Department of IT, Guru Nanak institutions technical campus (AUTONOMOUS), India. Author
  • Sk.Afreen B.Tech Students, Department of CSE, Guru Nanak institutions technical campus (AUTONOMOUS ), India. Author
  • T.Jessika B.Tech Students, Department of CSE, Guru Nanak institutions technical campus (AUTONOMOUS ), India. Author
  • K.Sai Darshan Naik B.Tech Students, Department of CSE, Guru Nanak institutions technical campus (AUTONOMOUS ), India. Author

DOI:

https://doi.org/10.62647/

Keywords:

CNN, Machine Learning

Abstract

Information and Communication Technologies have propelled social networking and communication, but cyber bullying poses significant challenges. Existing user-dependent mechanisms for reporting and blocking cyber bullying are manual and inefficient. Conventional Machine Learning and Transfer Learning approaches were explored for automatic cyber bullying detection. The study utilized a comprehensive dataset and structured annotation process. Textual, sentiment and emotional, static and contextual word embeddings, psycholinguistics, term lists, and toxicity features were employed in the Conventional Machine Learning approach. This research introduced the use of toxicity features for cyber bullying detection. Contextual embeddings of word Convolutional Neural Network (Word CNN) demonstrated comparable performance, with embeddings chosen for its higher F-measure. Textual features, embeddings, and toxicity features set new benchmarks when fed individually. This outperformed Linear SVC in terms of training time and handling high-dimensionality features. Transfer Learning utilized Word CNN for fine-tuning, achieving a faster training computation compared to the base models. Additionally, cyber bullying detection through Flask web was implemented, yielding an accuracy of 97.06%. The reference to the specific dataset name was omitted for privacy.

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Published

27-05-2025

How to Cite

Cyber Harassment Prediction In Social Media Using Word CNN. (2025). International Journal of Information Technology and Computer Engineering, 13(2), 1201-1206. https://doi.org/10.62647/