Abstract: Cyberbullying is the use of technology to harass, threaten or target another individual. Online bullying can be particularly damaging and upsetting since it is usually anonymous and it’s often hard to trace the bully. Sometimes cyberbullying can lead to issues like anxiety, depression, shame, suicide, etc. Most of the cyberbullying cases are not revealed to the public and the number of cases reported to the legal system is only few. Certain victims do not reveal their bully experiences out of shame or due to difficult procedures for reporting to the legal system. Our cyberbullying detection system aims to bring cases involving cyberbullying under control by detecting and warning the bully. Such cases are also reported to appropriate authorities, which can then be verified and necessary actions can be taken depending on the situation. The technology stack used for implementation include Flask, Scikit learn, Chat application APIs, Firebase, HTML, Javascript and CSS. The model was tested on classifiers like SVM, KNN, Logistic regression and Random Forest. F1 score was used as a metric to assess the four models. While analyzing the performances of these models, it was observed that Random Forest Classifier outperformed all the models. F1 score of 93.48% was achieved using the Random Forest Classifier.

Evangeline D, Amy S Vadakkan, Sachin R S, Aakifha Khateeb and Bhaskar C, “Cyberbullying Detection in Textual Modality” International Journal of Advanced Computer Science and Applications(IJACSA), 12(12), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121228

@article{D2021,
title = {Cyberbullying Detection in Textual Modality},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121228},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121228},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {12},
author = {Evangeline D and Amy S Vadakkan and Sachin R S and Aakifha Khateeb and Bhaskar C}
}