Abstract
This study delves into cyberbullying detection through the lens of bio inspired algorithms. Cyberbullying poses a significant threat to an individual's mental well being and societal harmony in the digital age. Due to online interactions' dynamic and nuanced nature, traditional detection methods often need to catch up. Drawing inspiration from natural processes, bio inspired algorithms offer a promising avenue to tackle this complex problem. This investigative study explores the efficacy of bio inspired algorithms, such as genetic algorithms, swarm intelligence, and artificial immune systems, in identifying cyberbullying instances across various online platforms. Through empirical analysis and experimentation, the study seeks to evaluate the performance of these algorithms in comparison to conventional approaches. Additionally, it endeavours to uncover the underlying mechanisms driving their effectiveness and potential areas for refinement. The results of this study can help design and develop accurate cyber bullying detection systems, thereby contributing to the creation of safer online environments for individuals of all ages.
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