A Multi-faceted Semi-Synthetic Dataset for Automated Cyberbullying Detection
This addresses the problem of inconsistent data for researchers in cyberbullying detection, though it is incremental as it focuses on dataset creation rather than novel detection methods.
The paper tackles the lack of standardized datasets for automated cyberbullying detection by creating an extensive semi-synthetic dataset that incorporates essential aspects like aggression, repetition, peer relationships, and intent to harm, and makes it publicly accessible to support broader research.
In recent years, the rising use of social media has propelled automated cyberbullying detection into a prominent research domain. However, challenges persist due to the absence of a standardized definition and universally accepted datasets. Many researchers now view cyberbullying as a facet of cyberaggression, encompassing factors like repetition, peer relationships, and harmful intent in addition to online aggression. Acquiring comprehensive data reflective of all cyberbullying components from social media networks proves to be a complex task. This paper provides a description of an extensive semi-synthetic cyberbullying dataset that incorporates all of the essential aspects of cyberbullying, including aggression, repetition, peer relationships, and intent to harm. The method of creating the dataset is succinctly outlined, and a detailed overview of the publicly accessible dataset is additionally presented. This accompanying data article provides an in-depth look at the dataset, increasing transparency and enabling replication. It also aids in a deeper understanding of the data, supporting broader research use.