Adaptive Ensemble Learning with a Fine-Tuned Framework for Cyberbullying Detection in Cross-Platform Social Media Environments
Received: 27 September 2025 | Revised: 3 November 2025, 26 November 2025, 1 December 2025, and 4 December 2025 | Accepted: 5 December 2025 | Online: 17 December 2025
Corresponding author: I. Anand Raj
Abstract
Recently, on social media platforms, cyberbullying has been a significant concern for organizations, individuals, and society, and its detection is gaining increasing attention. The simplicity of access to social media networks such as Instagram, Twitter, and Facebook has led to an exponential upsurge in the exploitation of individuals in the form of bullying, toxic comments, sexism, hateful messages, racism, harassment, aggressive content, etc. Machine learning models are trained to identify and flag latent cyberbullying content, in addition to recognizing behavioral patterns that are suggestive of cyberbullying. This study presents an Ensemble Learning for Cyberbullying Detection across Social Media Platforms Using Word Vector Representations (ELCDSMP-WVR) approach. Initially, text preprocessing is performed in three-levels. The GloVe method is employed for the word embedding process. An Ensemble Voting Classifier (EVC) integrates three advanced DL techniques, a Temporal Convolutional Network (TCN), a Graph Wasserstein Autoencoder (GWAE), and a Deep Belief Network (DBN), to improve the robustness of the classification process. Finally, the Black-Winged Kite Optimization Algorithm (BKA) is employed to improve overall performance. A comparison study of the ELCDSMP-WVR technique showed superior accuracies of 95.27% and 97.88% over existing approaches on two cyberbullying datasets.
Keywords:
cyberbullying detection, social media platforms, ensemble learning, black-winged kite optimisation algorithm, word vector representationDownloads
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