Article
DEEP LEARNING FRAMEWORK FOR MULTICLASS BOT CYBERBULLYING DETECTION ON SOCIAL MEDIA
Bot cyberbullying has emerged as a critical issue in digital communication, with over 59% of teens reporting experiences of online harassment and more than 70% of such cases occurring on social media platforms. Recent studies show that multi-class bot cyberbullying datasets often contain up to 25,000 labeled instances spanning categories such as insult, threat, racism, and sexism, frequently exhibiting severe class imbalance and linguistic variation. Manual detection methods suffer from subjectivity, inconsistent labeling, and an inability to scale with the rapid influx of user-generated content. Conventional machine learning approaches are limited by shallow feature representation, low recall on minority classes, and poor detection of implicit or masked abuse. Additionally, existing research often overlooks the integration of deep ensemble learning methods with optimized preprocessing and contextual analysis. To address these limitations, this study proposes a hybrid Multiclass Unmasking Bot Classification system that integrates a novel combination of feature-rich N-gram extraction with dual deep learning classifiers: a Deep Neural Network (DNN) and a Convolutional Neural Network (CNN). The process begins with dataset ingestion and detailed Exploratory Data Analysis (EDA) to assess data distribution and class imbalance. Text preprocessing follows, including tokenization, lemmatization, and noise removal. The cleaned data is then vectorized using TF-IDF with bi-gram support to capture both isolated and contextual word associations. The DNN is employed to capture deep semantic hierarchies, while the CNN is used to identify local linguistic patterns. This parallel dual-stream architecture ensures robust learning across diverse types of bot-generated cyberbullying. Finally, the trained models are evaluated for prediction accuracy and class-wise performance, significantly outperforming baseline classifiers in terms of precision, recall, and F1-score.
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