Instagram-Based Benchmark Dataset for Cyberbullying Detection in Arabic Text
Abstract
Background: the ability to use social media to communicate without revealing one’s
real identity has created an attractive setting for cyberbullying. Several studies targeted social media
to collect their datasets with the aim of automatically detecting offensive language. However, the
majority of the datasets were in English, not in Arabic. Even the few Arabic datasets that were
collected, none focused on Instagram despite being a major social media platform in the Arab world.
(2) Methods: we use the official Instagram APIs to collect our dataset. To consider the dataset as a
benchmark, we use SPSS (Kappa statistic) to evaluate the inter-annotator agreement (IAA), as well
as examine and evaluate the performance of various learning models (LR, SVM, RFC, and MNB).
(3) Results: in this research, we present the first Instagram Arabic corpus (sub-class categorization
(multi-class)) focusing on cyberbullying. The dataset is primarily designed for the purpose of
detecting offensive language in texts. We end up with 200,000 comments, of which 46,898 comments
were annotated by three human annotators. The results show that the SVM classifier outperforms the
other classifiers, with an F1 score of 69% for bullying comments and 85 percent for positive comments.
Description
Keywords
cyberbullying; offensive language; Arabic dialect
Citation
ALBayari, R. and Abdallah, S. (2022) “Instagram-Based Benchmark Dataset for Cyberbullying Detection in Arabic Text,” Data, 7(7), p. 83.