Ensemble based Satin Bower Bird Algorithms for Cyberbullying Detection

Cyberbullying has become a pervasive issue in online communities, necessitating the development of robust detection methods to mitigate its harmful effects. In this paper, we propose an innovative approach to cyberbullying detection inspired by the complex mating behaviors of Satin Bower Birds. Our algorithm leverages ensemble learning techniques to combine the strengths of multiple classifiers, enhancing the accuracy and reliability of detection. We present a comprehensive methodology that integrates bio-inspired optimization principles with machine learning frameworks, facilitating the extraction of diverse features from user interactions and content. Through extensive experimentation on real-world datasets, we demonstrate the effectiveness of our ensemble-based Satin Bower Bird algorithm in accurately identifying instances of cyberbullying behavior. The results highlight the potential of bio-inspired algorithms to address social challenges in online environments and underscore the importance of interdisciplinary research in advancing cyberbullying prevention efforts.