Towards Comprehensive Cyberbullying Detection: A Dataset Incorporating Aggressive Texts, Repetition, Peerness, and Intent to Harm

Published: 10 November 2023| Version 1 | DOI: 10.17632/wmx9jj2htd.1
Naveed Ejaz, Salimur Choudhury, Fakhra Razi


The increasing usage of social media networks has raised concerns about the growing frequency of cyberbullying incidents. 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 paper proposes a new dataset incorporating all four aspects of cyberbullying to address this gap. The text messages are sourced from a real dataset*, while the users' data is generated synthetically. The resulting dataset contains messages exchanged randomly among different pairs of users, thus inculcating repetition. Additionally, the degree of peerness, defined and calculated to measure the likelihood of two users being peers, is used. As a result, this dataset encompasses all four aspects of cyberbullying by providing repeated aggressive messages among users along with quantitative values of the degree of peerness and intent to harm.. Text Messages sourced from: Elsafoury, "Cyberbullying datasets," Mendeley. com, 2020. [Online]. Available: https://data. mendeley. com/datasets/jf4pzyvnpj/1.



Natural Language Processing, Applied Computer Science