A Multi-faceted Semi-Synthetic Dataset for Automated Cyberbullying
  Detection
Naveed Ejaz Fakhra Kashif Salimur Choudhury
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.