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Instagram-Based Benchmark Dataset for Cyberbullying Detection in Arabic Text

Author

Listed:
  • Reem ALBayari

    (Higher College of Technology, Abu Dhabi P.O. Box 25026, United Arab Emirates
    Faculty of Engineering and IT, The British University in Dubai, Dubai P.O. Box 345015, United Arab Emirates)

  • Sherief Abdallah

    (Faculty of Engineering and IT, The British University in Dubai, Dubai P.O. Box 345015, United Arab Emirates)

Abstract

(1) 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.

Suggested Citation

  • Reem ALBayari & Sherief Abdallah, 2022. "Instagram-Based Benchmark Dataset for Cyberbullying Detection in Arabic Text," Data, MDPI, vol. 7(7), pages 1-11, June.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:7:p:83-:d:845163
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    References listed on IDEAS

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    1. Yousri Marzouki & Fatimah Salem Aldossari & Giuseppe A. Veltri, 2021. "Understanding the buffering effect of social media use on anxiety during the COVID-19 pandemic lockdown," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-10, December.
    2. Vuk Batanović & Miloš Cvetanović & Boško Nikolić, 2020. "A versatile framework for resource-limited sentiment articulation, annotation, and analysis of short texts," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-30, November.
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