Cyberbullying Detection Based on Hybrid Ensemble Method using Deep Learning Technique in Bangla Dataset

Globalization is certainly a blessing for us. Still, this term also brought such things that are constantly not only creating social insecurities but also diminishing our mental health, and one of them is Cyberbullying. Cyberbullying is not only a misuse of technology but also encourages social harassment among people. Research on Cyberbullying detection has gained increasing attention nowadays in many languages, including Bengali. However, the amount of work on the Bengali language compared to others is insignificant. Here we introduce a Hybrid ensemble method using a voting classifier in Bangla Cyberbullying detection and compare this with traditional Machine Learning and Deep Learning Classifiers. Before implementation, Exploratory Data Analysis was performed on the dataset to gather better insight. There are lots of papers that have already been published in other languages where it is seen that the hybrid approach provides better outcomes compared to traditional methods. Thus, we propose a highly well-driven method for Cyberbullying detection on the Bangla dataset using the hybrid ensemble method by voting classifier. The overall deployment consists of three Machine Learning classifiers, three Deep Learning classifiers, and a Hybrid approach using the voting classifier. Finally, the Hybrid ensemble method yields the best performance with an accuracy of 85%, compared with other Machine and Deep Learning methods.

@article{Ahmed2023,
  title     = {Cyberbullying Detection Based on Hybrid Ensemble Method using Deep Learning Technique in Bangla Dataset},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {9},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Md. Tofael Ahmed and Afroza Sharmin Urmi and Maqsudur Rahman and Abu Zafor Muhammad Touhidul Islam and Dipankar Das and Md. Golam Rashed},
  doi       = {10.14569/IJACSA.2023.0140958},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140958}
}