A Comprehensive Dataset for Automated Cyberbullying Detection

Published: 22 January 2024| Version 2 | DOI: 10.17632/wmx9jj2htd.2
Naveed Ejaz, Salimur Choudhury, Fakhra Razi


Cyberbullying is characterized by aggressive, repetitive, and intentional communication among peers. However, most existing datasets for cyberbullying detection only focus on aggressive texts classified as aggressive or non-aggressive, disregarding the other three aspects of cyberbullying. This dataset is a comprehensive dataset that incorporates the four aspects of Cyberbullying. This dataset is an updated version of the dataset presented in our paper[1] and has been developed using the same methodology. In this updated version, we present complete and enhanced data and the code to generate data. The aggressive and non-aggressive messages compiled from different sources [2,3] have also been shared. If you use this dataset, please cite our paper [1] [1] Ejaz, Naveed, Fakhra Razi, and Salimur Choudhury. "Towards comprehensive cyberbullying detection: A dataset incorporating aggressive texts, repetition, peerness, and intent to harm." Computers in Human Behavior (2023): 108123. Text Messages sourced from: [2] Elsafoury, "Cyberbullying datasets," Mendeley. com, 2020. [Online]. Available: https://data. mendeley. com/datasets/jf4pzyvnpj/1. [3] R. Kumar, A. N. Reganti, A. Bhatia, and T. Maheshwari, "Aggression-annotated Corpus of Hindi-English Code-mixed Data," in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan, May 7-12, 2018.



Natural Language Processing, Applied Computer Science