README.md

metadata

license: cc-by-sa-4.0
task_categories:
  - text-classification
  - question-answering
  - zero-shot-classification
language:
  - bn
tags:
  - communal
  - violence
  - dataset
  - classification
  - bengali
  - low-resource
  - annotator-disagreement
  - multi-label
  - bengali
  - bangla
pretty_name: DANGA
size_categories:
  - 10K<n<100K

DANGA: Discourse Analysis of Group Aggression

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

	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 from the BanSect framework:

• 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-NC 4.0. It is intended for research purposes only.

Ethical Considerations

This dataset contains violent, hateful, and offensive language in Bengali. The texts include communal slurs, threats, and dehumanizing content directed at religious, ethnic, and cultural communities. This content is preserved for research purposes, to build systems that can detect and mitigate such violence.

• The dataset should not be used to generate or amplify hate speech
• All annotators have been anonymized (identified only as Annotator A-H)
• The data was collected from publicly available social media comments, anonymizing mentions where it appeared