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Related Concept Videos

Types of Toxins01:36

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Related Experiment Videos

Bangla-ToCo: A context-aware dataset for Bengali toxic comment detection.

Sayma Akter Rupa1, Md Musfique Anwar2, Nadia Afrin Ritu2

  • 1R.P. Shaha University, Bangladesh.

Data in Brief
|December 17, 2025
PubMed
Summary

This study introduces the first context-aware Bengali toxic comment dataset, crucial for developing automatic moderation systems. The dataset aids in understanding online abuse in Bengali, a low-resource language, by preserving conversational context.

Keywords:
Low-resource languageNatural Language ProcessingSocial mediaToxicity classification

Related Experiment Videos

Area of Science:

  • Computational Linguistics
  • Social Sciences
  • Natural Language Processing

Background:

  • Abusive and toxic comments are a growing online concern, especially for low-resource languages like Bengali.
  • Existing resources for Bengali lack conversational context, hindering accurate analysis of online comments.
  • There is a need for reliable automatic moderation systems for Bengali social media.

Purpose of the Study:

  • To introduce a novel context-aware dataset for identifying toxic comments in Bengali.
  • To enable more accurate interpretation of comments by preserving conversational context.
  • To support the development of NLP tools for Bengali, a low-resource language.

Main Methods:

  • Collected 1004 Bengali news comments from Bangladeshi news portals' Facebook pages.
  • Preserved conversational context by including news titles, article metadata, and surrounding comments.
  • Annotated comments into 'Toxic' and 'Non-Toxic' categories using independent human annotators and majority voting.

Main Results:

  • Developed the first context-aware Bengali toxic comment dataset.
  • Created a balanced dataset suitable for supervised learning and benchmarking.
  • The dataset includes news titles, metadata, target comments, and conversational context.

Conclusions:

  • This dataset is a valuable resource for Bengali NLP and social discourse analysis.
  • It facilitates research in abusive content detection and sentiment analysis for Bengali.
  • The context-aware approach improves the understanding of online toxicity in low-resource languages.