Title: Cyberbullying detection and recognition using deep learning: SVM classification

Authors: D. Maalini; I. Nandhini; S. Nelson; K. Umamaheswari

Addresses: Department of Information Technology, V.S.B. Engineering College, Karur, India ' Department of Information Technology, V.S.B. Engineering College, Karur, India ' Department of Information Technology, V.S.B. Engineering College, Karur, India ' Department of Computer Science and Engineering, V.S.B. Engineering College, Karur, India

Abstract: Strong computational techniques are required for the growing importance of cyberbullying detection on social media platforms. There is a risk that some users may take advantage of these possibilities to humiliate, degrade, abuse, and harass other individuals. Research provides a comparative examination of various distinct deep learning procedures with the purpose of testing and evaluating the performance of deep learning methods in relation to a well-known worldwide Twitter dataset. The detection of abusive tweets and the discovery of remedies to the problems that are now being faced have both been accomplished via the use of attention-based deep learning algorithms. In order to extract the features, an application of the word2vec technique that was concatenated with CBOW was used. The proposed model achieves an accuracy rate of 90% for the given dataset.

Keywords: cyberbully; RNN; CNN; LSTM; BiLSTM; word2vec; text classification.

DOI: 10.1504/IJESDF.2026.153333

International Journal of Electronic Security and Digital Forensics, 2026 Vol.18 No.3, pp.336 - 347

Received: 08 Jan 2024
Accepted: 19 Mar 2024
Published online: 01 May 2026 *

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