README.md

---
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:

```json
{
  "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](https://creativecommons.org/licenses/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