Cyberbullying Detection, Categorization and Severity Classification in Networking Platforms for Teenagers and Young Adults
Cyberbullying Detection, Categorization and Severity Classification in Networking Platforms for Teenagers and Young Adults |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-9 |
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| Year of Publication : 2025 | ||
| Author : Sakshi Khanapure, Shilpa Deshpande, Brishti Basu, Arundhati Korlahalli, Anandita Rathod | ||
| DOI : 10.14445/22315381/IJETT-V73I9P104 | ||
How to Cite?
Sakshi Khanapure, Shilpa Deshpande, Brishti Basu, Arundhati Korlahalli, Anandita Rathod,"Cyberbullying Detection, Categorization and Severity Classification in Networking Platforms for Teenagers and Young Adults", International Journal of Engineering Trends and Technology, vol. 73, no. 9, pp.32-47, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I9P104
Abstract
The emergence of various social networking platforms has made it effortless for individuals to connect globally and exchange their hobbies and interests. Especially among teenagers and young adults, having a social media presence and participating in multiplayer games has become a way of life. Cyberbullying is a prevalent crime that misuses the feature of staying anonymous online to bully and threaten people through digital platforms. Cyberbullying detection is hence the need of the hour. This paper offers a system to enable Cyberbullying Detection, Categorization, and Severity classification (Cb-DCS) of social media comments consisting of text along with emojis. The research focuses on detecting cyberbullying, categorizing it according to type, and classifying it according to severity with Machine Learning complemented by Deep Learning strategies. More specifically, the proposed work makes use of algorithms that include Bidirectional Long Short-Term Memory (BiLSTM), Multinomial Naïve Bayes (MNB), Bidirectional Encoder Representations from Transformers, and Support Vector Machine (SVM) for detecting cyberbullying. The categorization of cyberbullying comments is explored using techniques that include MNB, SVM, Random Forest (RF), and Convolutional Neural Networks. The severity identification of the comments is carried out using SVM, RF and MNB. The Cb-DCS system uses emoji embedding to extract the sentiment of the emojis. Experimental results show that the performances of MNB with Global Vectors (GloVe) for representing words, RF, and SVM are superior to the other corresponding techniques concerning accuracy and F1-score for the tasks of cyberbullying detection, categorization, and severity classification, respectively.
Keywords
Cyberbullying Categorization, Emoji, Machine Learning, Severity, Online Platforms.
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