K. Swayam Prabha, K. Murali Krishna, G. Ranjith Kumar, I. Laxman, D. Arun Kumar 02 08 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 multiclass bot cyberbullying datasets often contain up to 25000 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 usergenerated 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 featurerich Ngram 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 TFIDF with bigram 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 dualstream architecture ensures robust learning across diverse types of botgenerated cyberbullying Finally the trained models are evaluated for prediction accuracy and classwise performance significantly outperforming baseline classifiers in terms of precision recall and F1score Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026