Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kumar, Ravinder; Sharma, Dimpal; Kumar, Ajay; Hemrajani, Naveen; Poonia, Ramesh Chandra Title A name given to the resource An Efficent Deep Learning Framework for Cyberbullying Detection Using DistilBERT and Sentiment Analysis Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2025 - Proceedings;pp.185-190 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ETNCC66224.2025.11299715" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ETNCC66224.2025.11299715</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105031883634?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105031883634?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kumar R., Jecrc University, Department of Computer Science &amp; Engineering, Jaipur, India; Sharma D., Jecrc University, Department of Computer Science &amp; Engineering, Jaipur, India; Kumar A., Jecrc University, Department of Computer Science &amp; Engineering, Jaipur, India; Hemrajani N., Jecrc University, Department of Computer Science &amp; Engineering, Jaipur, India; Poonia R.C., Department of Computer Science, Christ University, Bangalore, India Description An account of the resource Particularly because of the complex and changing character of online communication, which hampers conventional detection strategies, the frequency of cyberbullying presents a significant threat to mental health and well-being in the digital age. This article presents a fast deep learning approach to improve cyberbullying detection by combining sentiment analysis with a lightweight transformer model, DistilBERT. This work intends to increase classification performance by using sentiment-based features and using DistilBERT's language and contextual awareness. Unlike conventional approaches and simpler machine learning methods, which can depend on feature extraction techniques like Bag of Words (BOW) or TF-IDF, the proposed model directly leverages contextual embeddings. Moreover, DistilBERT provides a balance between speed and performance unlike deep learning models like CNN, BLSTM, and LSTM, which could suffer with computational efficiency. Experimental results demonstrating remarkable accuracy and recall on many different datasets indicate the effectiveness of our hybrid approach. demonstrating a significant rise in cyberbullying detection over conventional methods, to evaluate performance criteria including computational efficiency, accuracy, and F1-score. With an outstanding 93.7 % accuracy, the proposed model exceeded earlier evaluated methods on this dataset. 2025 IEEE. Subject The topic of the resource Deep Learning; Detection Cyberbullying; DistilBERT; Natural Language Processing; Sentiment Analysis Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152565-1; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online