Content Warning: This dataset contains violent, hateful, and severely offensive language in Bengali, including communal slurs, dehumanizing rhetoric, threats, and incitement to violence targeting religious, ethnic, and cultural communities. It is intended solely for research purposes (hate speech detection, content moderation, NLP). Do not use this dataset to generate, promote, or amplify harmful content.

This dataset has been released as part of the Adaption Competition, with support from Adaptive Data by Adaption, whose initiative motivated us to formalize, restructure, and substantially update this dataset prior to its open release.

BanDANGA: A Bangla Dataset on Aggressive Narratives and Group-based Attacks

দাঙ্গা (DANGA) is an expert-annotated Bengali dataset of 12,720 social media texts classified for communal and sectarian violence. It captures violence across four identity dimensions: religion, ethnicity, socioculture, and nondenominational. Each dimensions were annotated with up to four expression types: derogation, antipathy, prejudication, and repression. The dataset includes full annotator disagreement metadata with individual votes, resolution strategies, and anonymized annotator pairs. This makes it suitable for multi-label classification, preference learning (DPO), and LLM fine-tuning on low-resource hate speech detection.

Dataset Summary

Authors & Attribution

DANGA was developed by Istiak Shihab and Nazia Tasnim as part of a broader effort to advance resources for the Bengali language.

The work was carried out in collaboration with Bengali.AI, a non-profit focused on building and promoting open technologies for Bengali.

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Dataset Summary

	Count
Total samples	12,720
Violent	4,459 (35.1%)
Non-violent	8,261 (64.9%)
Multi-category violent	146
Annotator pairs	4 (8 annotators)
Disputed samples	4,963 (39.0%)
Expert-resolved disputes	1,553

Schema

Each record is a JSON object with the following structure:

{
  "text": "হিন্দু মালুরা বাংলাদেশে বসবাস করে ...",
  "violent": true,
  "labels": {
    "religion": ["derogation", "antipathy", "prejudication"],
    "ethnicity": [],
    "socioculture": [],
    "nondenominational": ["prejudication"]
  },
  "annotation": {
    "disputed": true,
    "resolution": "third-party",
    "annotators": ["C", "D"],
    "votes": {
      "annot_1": {
        "religion": ["derogation", "antipathy", "prejudication"],
        "ethnicity": [],
        "socioculture": [],
        "nondenominational": ["derogation", "antipathy", "prejudication"]
      },
      "annot_2": {
        "religion": ["derogation", "antipathy", "prejudication"],
        "ethnicity": [],
        "socioculture": [],
        "nondenominational": []
      }
    }
  }
}

Fields

Field	Type	Description
text	string	Bengali social media text (YouTube comments)
violent	bool	Whether the text contains any violence expression
labels	object	Gold-standard labels across 4 identity dimensions
annotation	object	Full annotator disagreement metadata

Identity Dimensions

Dimension	Column	Target Communities	Examples
Religio-communal	religion	Religious identity groups	Muslim, Hindu, Christian, Ahmadia, Shia, Atheist, Baul
Ethno-communal	ethnicity	Ethnic identity groups	Bihari, Rohingya, Chakma, Adibashi
Sociocultural	socioculture	Regional/geographic/cultural identity	Sylheti, Kashmiri, Brahmanbaria, Cultural Baul
Nondenominational	nondenominational	Individual, gender, political targets	Misogyny, homophobia, political entities, government

Expression Types (Degree of Violence)

Each identity dimension is annotated with zero or more expression types:

Expression	Description	Count
Derogation	Communal slurs, incivility, dehumanization, bullying	2,212
Prejudication	False accusation, victim blaming, stereotyping, justifying mistreatment	2,125
Antipathy	Alienation, deportation, stripping rights, internalized hatred	827
Repression	Direct threats, incitement to harm, encouraging violence	517

Annotation Metadata

Each sample includes full disagreement provenance:

Field	Values	Description
disputed	true / false	Whether annotators disagreed
resolution	sided_with_X / third-party / null	How the dispute was resolved
annotators	["X", "Y"]	Anonymized annotator pair (A–H)
votes.annot_1	labels object	First annotator's original labels
votes.annot_2	labels object	Second annotator's original labels

Resolution distribution (4,963 disputed samples):

Resolution	Count
Sided with first annotator	2,205
Sided with second annotator	1,205
Third-party expert label	1,553

Taxonomy

The dataset employs a 4×4 orthogonal taxonomy:

• 4 Identity dimensions (WHO is targeted): Religio-communal, Ethno-communal, Sociocultural, Nondenominational
• 4 Expression types (HOW violence is expressed): Derogation, Antipathy, Prejudication, Repression
• Posts can have multiple identity categories and multiple expression types simultaneously (multi-label)

This produces a theoretical space of 16 fine-grained violence classes.

Source

Metric	Value
Language	Bengali (বাংলা)
Source	YouTube, Facebook, Newspaper comments

Intended Uses

• Violence detection in Bengali social media
• Multi-label classification research
• Annotator disagreement modeling and calibration
• LLM fine-tuning for hate speech and communal violence tasks
• Preference learning (DPO/RLHF) using annotator votes as chosen/rejected pairs
• Cross-lingual transfer for low-resource language hate speech detection

License

This dataset is released under CC BY-SA 4.0. It is intended for research purposes only.

Anonymization Policy

To protect the privacy of individuals whose content appears in this dataset, the following anonymization measures were applied:

• Annotator identities are fully anonymized. All annotators are referred to only by a randomly assigned letter (A–H). No names, institutional affiliations, or demographic information about annotators are disclosed.
• Author/poster identities from source platforms (YouTube, Facebook, newspaper comment sections) are not included in the dataset. Usernames and profile references have been removed or replaced.
• Personal mentions within text (e.g., tagged usernames, phone numbers, identifiable personal details) were removed or masked where detected during preprocessing.
• The raw source URLs or post IDs that could be used to re-identify individuals are not released as part of this dataset.

Researchers who discover re-identification risks are encouraged to contact the authors.

Ethical Considerations

This content is preserved for research purposes, specifically to build systems that can detect and mitigate such violence. The following guidelines apply:

• The dataset must not be used to generate, promote, or amplify hate speech or communal violence
• The dataset is intended for research use only (NLP, content moderation, computational social science)
• All annotators have been fully anonymized (see Anonymization Policy above)
• The data was collected from publicly available social media comments; personal identifiers have been removed
• Users of this dataset are expected to adhere to responsible AI and research ethics guidelines