Abstract
Social media has become an integral part of daily life, with platforms like Twitter serving as popular outlets for users to share information and express grievances. While social media offers numerous benefits, it can also be misused for cyberbullying, such as insults and harassment. This highlights the importance of detecting and mitigating cyberbullying to ensure a safe online environment. Detecting sarcasm in social media posts is challenging and requires advanced automated systems. To address this, a deep learning-based approach was developed using a Convolutional Neural Network (CNN) for analysis and an Attention Mechanism-based Bidirectional Long Short-Term Memory with Gated Recurrent Unit (AM-BLSTM-GRU) for prediction. Sarcasm detection datasets were gathered from sources like Kaggle and News Headlines. Standard NLP-based auxiliary features were extracted, and an embedded CNN model refined these features into feature vectors. The AM-BLSTM-GRU model then performed sarcasm detection and sentiment classification. The Enhanced Sinogramic Red Deer (ESRD) optimizer was utilized to optimize the classifier parameters effectively. The proposed model outperformed existing deep learning approaches on popular benchmark datasets and evaluation metrics, demonstrating its effectiveness in detecting cyberbullying. This approach was validated using well-known datasets and metrics, confirming its efficacy in identifying and reducing harmful online behaviors.