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Cited by 30Hate Speech and Cyberbullying DetectionSentiment Analysis and Opinion MiningText and Document Classification Technologies

Instagram-Based Benchmark Dataset for Cyberbullying Detection in Arabic Text

Open access
ALBayari, Reem, Abdallah, Sherief·2022·Data
DOI 10.3390/data7070083
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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.

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35 of 35 references verified0 citation errors · 0 not checkable

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  • The review found that existing studies on cyberbullying text detection used sparse data representation to efficiently store and process datasets, especially those with many missing values.

    “The review found that existing studies on cyberbullying text detection used sparse data representation to efficiently store and process datasets, especially those with many missing values [120].”

    From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [120]

  • Datasets may be limited due to factors such as incomplete data percentages, inadequate details for comprehensive analytical evaluations, and the utilization of a small dataset for training learning models.

    “The dataset might be viewed as limited due to factors such as incomplete data percentages, inadequate details for comprehensive analytical evaluations, and the utilization of a small dataset for training learning models [120].”

    From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [120]

  • Small datasets impede the direct comparison of progress and the assessment of applicability, as they do not adequately capture the necessary complex social dynamics.

    “Small datasets impede the direct comparison of progress and the assessment of applicability, as they do not adequately capture the necessary complex social dynamics [120].”

    From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [120]

  • The utilization of cutting-edge methods is constrained by restricted access to top-tier data, leading to the creation of small, varied datasets that impede straightforward comparisons of advancements.

    “The utilization of cutting-edge methods is constrained by restricted access to top-tier data, leading to the creation of small, varied datasets that impede straightforward comparisons of advancements [120].”

    From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [120]

  • Using crowdsourcing techniques to replicate actual bullying instances in a controlled laboratory environment can produce credible data that augment existing datasets and boost classifier accuracy.

    “Using crowdsourcing techniques to replicate actual instances of bullying within a controlled laboratory environment can produce credible data, augmenting existing datasets and boosting the accuracy of classifiers [120].”

    From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [120]

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Research works printed but not linked: 3

  1. Sanchez, H. Twitter Bullying Detection. Homo 2011, 12, 15.
  2. Hosam, O. Toxic comments identification in arabic social media. Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 2019, 11, 219-226.
  3. Al-Harbi, S.; Almuhareb, A.; Al-Thubaity, A.; Khorsheed, M.S.; Al-Rajeh, A. Automatic Arabic Text Classification. In Proceedings of The 9th International Conference on the Statistical Analysis of Textual Data. Available online: https://www.researchgate.net/ publication/313363859_Automatic_Arabic_Text_Classification (accessed on 20 May 2022).

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Authors
ALBayari, R., & Abdallah, S.
Affiliations
  • Higher College of Technology, Abu Dhabi P.O. Box 25026, United Arab Emirates
  • British University in Dubai (AE)
ORCID
Venue
Data
Publisher
MDPI AG
Published
2022-06-22
Volume
7
Issue
7
Pages
83
ISSN
2306-5729 (ISSN-L)
DOI
10.3390/data7070083
OpenAlex
W4283321753
Cited by
30
References
35
Citations by year
2026: 2 · 2025: 8 · 2024: 10 · 2023: 9 · 2022: 1
FWCI
2.30
Type
journal article
Language
en
Open access
Licence
Locations
  • Data (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
British University in Dubai
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
  • Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
Keywords
cyberbullying detection, Arabic text, Instagram, offensive language detection, social media comments, annotated corpus, benchmark dataset, inter-annotator agreement

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