---
language:
- bn
license: mit
task_categories:
- text-classification
- image-classification
- video-classification
tags:
- fake-news-detection
- misinformation
- multimodal
- bengali
- bangla
- trimodal
- nlp
- computer-vision
- satire-detection
- clickbait-detection
pretty_name: "BTMD: Bengali Trimodal Misinformation Dataset"
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/metadata.parquet
---

# BTMD: Bengali Trimodal Misinformation Dataset

## Dataset Summary

**BTMD** (Bengali Trimodal Misinformation Dataset) is a large-scale, manually annotated multimodal dataset for Bengali (Bangla) fake news detection. It contains **6,058** carefully curated news instances spanning three modalities — **text**, **image**, and **video** — collected from diverse Bengali online sources.

The dataset supports both **binary classification** (Real vs. Fake) and **fine-grained misinformation classification** (Real, Misinformation, Satire, Clickbait), making it a comprehensive benchmark for evaluating multimodal misinformation detection models in a low-resource language setting.

BTMD covers **11 news categories** and was constructed through a rigorous multi-stage pipeline involving crowdsourced data collection, source verification, expert annotation, and inter-annotator agreement analysis.

## Supported Tasks

| Task | Description | Labels |
|------|-------------|--------|
| **Binary Classification** | Distinguish real from fake news | `Real`, `Fake` |
| **Fine-grained Classification** | Classify the type of misinformation | `Real`, `Misinformation`, `Satire`, `Clickbait` |

## Languages

- **Bengali (Bangla)** — ISO 639-1: `bn`

## Dataset Structure

### Data Fields

| Field | Type | Description |
|-------|------|-------------|
| `id` | `string` | Unique identifier (UUID) for each news instance |
| `heading` | `string` | News headline (may be null for image/video-only instances) |
| `text` | `string` | Full article text content (may be null) |
| `image_path` | `string` | Relative path to the associated image file(s). Multiple images are semicolon-separated (e.g., `images/img1.png;images/img2.png`). May be null for text-only or video-only instances. |
| `video_path` | `string` | Relative path to the associated video file (may be null) |
| `label` | `string` | Binary label: `Real` or `Fake` |
| `multi_category` | `string` | Fine-grained label: `Real`, `Misinformation`, `Satire`, or `Clickbait` |
| `source_category` | `string` | Source platform (e.g., `Facebook`, `YouTube`, `Website`, `Newspaper`) |
| `category` | `string` | News domain category (e.g., `Politics`, `Health`, `Science`) |

### Data Splits

The dataset is provided as a **single unsplit file** with 6,058 instances. The original paper employs **5-fold cross-validation** for evaluation. Users are free to define their own splits as needed.

| Split | Instances |
|-------|-----------|
| Full Dataset | 6,058 |

### Modality Availability

Each instance contains at least one modality (text, image, or video). The textual modality includes news headlines and article content, while the visual modality consists of images associated with each news item.

| Modality | Instances |
|----------|-----------|
| Has Text (heading or article) | 4,457 |
| Has Image | 4,289 |
| Has Video | 1,383 |

## Label Definitions

| Label | Definition |
|-------|-----------|
| **Real** | Factually accurate content whose textual claims and associated visual or video content can be verified using credible sources. |
| **Misinformation** | False or misleading content presented as factual information, including fabricated claims, manipulated information, or authentic media shared in a misleading context. |
| **Satire** | Content created primarily for humor, irony, or social commentary that may be mistaken for genuine news when shared without its original context. |
| **Clickbait** | Content containing sensational or misleading headlines, thumbnails, or descriptions intended to attract attention while inaccurately representing the underlying information. |

## Dataset Statistics

### Binary Class Distribution

| Binary Class | Count | Percentage |
|-------------|-------|------------|
| Real | 3,029 | 50.0% |
| Fake | 3,029 | 50.0% |
| **Total** | **6,058** | **100.0%** |

### Fine-grained Category Distribution

| Category | Count | Percentage |
|----------|-------|------------|
| Real | 3,029 | 50.0% |
| Misinformation | 1,859 | 30.7% |
| Satire | 594 | 9.8% |
| Clickbait | 576 | 9.5% |

### News Domain Distribution

| Category | Total | Real | Fake | Misinfo. | Satire | Clickbait |
|----------|-------|------|------|----------|--------|-----------|
| Politics | 879 | 409 | 470 | 327 | 131 | 12 |
| Technology | 679 | 348 | 331 | 84 | 164 | 83 |
| Entertainment | 636 | 345 | 291 | 200 | 55 | 36 |
| Sports | 613 | 313 | 300 | 214 | 73 | 13 |
| International | 610 | 302 | 308 | 230 | 36 | 42 |
| Science | 579 | 286 | 293 | 121 | 0 | 172 |
| Environment | 551 | 291 | 260 | 170 | 0 | 90 |
| Religion | 527 | 264 | 263 | 154 | 99 | 10 |
| Health | 402 | 201 | 201 | 101 | 0 | 100 |
| Education | 362 | 161 | 201 | 167 | 34 | 0 |
| Miscellaneous | 220 | 109 | 111 | 91 | 2 | 18 |

### Modality Co-occurrence

| Modality Combination | Count | Percentage | Real | Fake |
|---------------------|-------|------------|------|------|
| Text + Image | 2,735 | 45.15% | 1,363 | 1,372 |
| Image Only | 1,473 | 24.31% | 744 | 729 |
| Text + Video | 1,176 | 19.41% | 601 | 575 |
| Text Only | 467 | 7.71% | 219 | 248 |
| Video Only | 126 | 2.08% | 72 | 54 |
| Text + Image + Video | 81 | 1.33% | 30 | 51 |

### Source Distribution

| Source | Total | Real | Fake |
|--------|-------|------|------|
| Facebook | 3,627 | 1,558 | 2,069 |
| YouTube | 831 | 228 | 603 |
| Website | 589 | 498 | 91 |
| Newspaper | 532 | 476 | 56 |
| News Channel | 274 | 266 | 8 |
| Miscellaneous | 97 | 0 | 97 |
| TikTok | 55 | 0 | 55 |
| Twitter | 37 | 3 | 34 |
| Instagram | 14 | 0 | 14 |
| Blog | 2 | 0 | 2 |

### Text and Video Statistics

| Statistic | Word Count | Video Duration (s) |
|-----------|-----------|-------------------|
| Minimum | 1 | 3.87 |
| Maximum | 2,738 | 300.12 |
| Mean | 156.80 | 87.47 |
| Median | 59 | 72.62 |
| Std. Dev. | 211.91 | 69.79 |
| Q1 | 11 | 26.68 |
| Q3 | 247 | 128.35 |

## Data Collection

### Collection Pipeline

BTMD was constructed through a structured multi-stage pipeline:

1. **Crowdsourced Acquisition**: A custom Telegram bot collected news content (text, images, videos, URLs) from volunteers across multiple undergraduate and graduate programs at the American International University-Bangladesh (AIUB).

2. **Source Verification**: Each submitted news item was independently verified by trained annotators through cross-referencing with credible sources, including established news outlets, official reports, fact-checking organizations, and trusted online platforms.

3. **Annotation**: Five trained annotators with domain expertise labeled each instance using a custom cross-platform annotation tool. Annotations include binary labels, fine-grained categories, and annotator confidence scores.

4. **Quality Assurance**: Instances with confidence scores ≤ 70% were re-annotated under blind review by all five annotators. Final labels were determined by majority voting (≥ 3/5 agreement).

### Source Diversity

Data was collected from diverse online sources including social media platforms (Facebook, YouTube, TikTok, Twitter, Instagram), news portals, newspapers, news channels, blogs, and online forums.

### Annotation Tool

The custom annotation platform is publicly available at: [https://github.com/Faysal1000/fake-news-annotation-tool](https://github.com/Faysal1000/fake-news-annotation-tool)

## Annotation Quality

### Inter-Annotator Agreement

Agreement was evaluated on a stratified 500-instance subset using Cohen's κ (pairwise) and Fleiss' κ (five-rater), under a blind re-labeling protocol.

| Annotation Scheme | Class | Cohen's κ | Fleiss' κ |
|-------------------|-------|-----------|-----------|
| Binary | Overall | 0.848 | 0.848 |
| Multi-class | Real | 0.825 | 0.825 |
| Multi-class | Misinformation | 0.814 | 0.814 |
| Multi-class | Satire | 0.842 | 0.842 |
| Multi-class | Clickbait | 0.802 | 0.802 |
| Multi-class | Overall | 0.822 | 0.822 |

All κ values indicate **almost perfect agreement** (κ > 0.80) according to the Landis and Koch benchmark.

## Usage

### Loading the Dataset

```python
from datasets import load_dataset

# Load the full dataset
dataset = load_dataset("Faysal4200/BTMD")

# Access a sample
sample = dataset["train"][0]
print(sample["heading"])       # News headline
print(sample["label"])         # Binary label: Real or Fake
print(sample["multi_category"])# Fine-grained label
print(sample["image_path"])    # Path to image file (if available)
print(sample["video_path"])    # Path to video file (if available)
```

### Loading with Images

```python
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

dataset = load_dataset("Faysal4200/BTMD")

# Load image(s) for a sample
# Note: image_path may contain multiple semicolon-separated paths
sample = dataset["train"][0]
if sample["image_path"]:
    image_paths = sample["image_path"].split(";")
    for img_path in image_paths:
        img = Image.open(hf_hub_download(
            repo_id="Faysal4200/BTMD",
            filename=img_path.strip(),
            repo_type="dataset"
        ))
        img.show()
```

### 5-Fold Cross-Validation (as in the original paper)

```python
from datasets import load_dataset
from sklearn.model_selection import StratifiedKFold

dataset = load_dataset("Faysal4200/BTMD", split="train")

skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
labels = dataset["label"]

for fold, (train_idx, val_idx) in enumerate(skf.split(range(len(dataset)), labels)):
    train_set = dataset.select(train_idx)
    val_set = dataset.select(val_idx)
    print(f"Fold {fold+1}: Train={len(train_set)}, Val={len(val_set)}")
```

## Citation

If you use this dataset in your research, please cite:

```bibtex
@article{ahmmed2025mome,
  title     = {MoME-BanglaFake: A Mixture-of-Modality-Experts Framework for Bengali Fake News Detection Using a Novel Trimodal Dataset},
  author    = {Ahmmed, Faysal and Rafsan, Resadus Salehin and Akther, Airin and Mansib, Muhtadi and Esika, Ainea Esrat and Mridha, F. M.},
  journal   = {International Journal of Information Management Data Insights},
  year      = {2026},
  note      = {Paper under review. Citation details will be updated upon publication.}
}
```

> **Note**: The full citation with volume, pages, and DOI will be updated once the paper is published in *International Journal of Information Management Data Insights*.

## License

This dataset is released under the [MIT License](https://opensource.org/licenses/MIT).

## Contact

For questions or issues regarding this dataset, please contact:
- **Faysal Ahmmed** — [22-47069-1@student.aiub.edu](mailto:22-47069-1@student.aiub.edu) or [faysalahmmed4200@gmail.com](mailto:faysalahmmed4200@gmail.com)
- **F. M. Mridha** — [firoz.mridha@aiub.edu](mailto:firoz.mridha@aiub.edu)

**Affiliation**: Department of Computer Science, American International University-Bangladesh (AIUB), Dhaka-1229, Bangladesh.