CCBD-Net: A Cloud-Based Approach for Cyberbullying Detection on Twitter Using Character-Level Features withCBCNN Model
Keywords:
Cyberbullying, Twitter, social media, Cloud computing, Character-level features, CLNG feature extraction, CBCNN model, Online safety, Hate speech, Text classification, Ethical considerations.Abstract
Cyberbullying has become a pervasive issue on social media platforms like Twitter, posing significant societal challenges due to its harmful impact on users' emotional well-being and the vast volume of data involved, making manual monitoring unfeasible. This research proposes CCBD-Net, a systematic approach for automating cyberbullying detection using cloud computing and Twitter data. The study begins with the collection of Twitter data, followed by data preprocessing, character-level feature extraction, and the utilization of a Convolutional Neural Network (CNN) model for cyberbullying prediction.The character level N-gram (CLNG)feature extraction involves extracting character-level features, which delve into the nuances of language use by analyzing individual characters within the text. To predict cyberbullying instances, the CCBD-Net employs character basedbi-directional CNN (CBCNN) model, a deep learning architecture suitable for text classification tasks. The model leverages the CLNG features extracted to make binary predictions regarding the presence and intensity of cyberbullying in each Twitter post or interaction.The Proposed CCBD-Net demonstrates an average improvement of approximately 2.083% in accuracy, 2.034% in precision, 1.646% in recall, 0.790% in F1-score, and 2.788% in sensitivity when compared to existing AI-based methods, highlighting its effectiveness in enhancing various performance metrics for CBD classification.
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