# HinglishMemeX

*A Code-Mixed Multimodal Dataset for Misinformation and Satire Detection in Indian Memes*

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## Overview
HinglishMemeX is a curated multimodal dataset consisting of **1,370 Indian social-media memes** that combine **images + Hinglish (Hindi+English code-mixed) text**. Each meme is paired with:
- OCR‑extracted Hinglish text
- A distilled **English factual claim**
- A **supporting evidence URL** (from a trusted fact-checking source)
- A **veracity label**: `real`, `fake`, `satire`, or `partially_true`

This dataset is designed for **misinformation detection**, **satire identification**, **multimodal classification**, and **retrieval‑augmented fact verification**.

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## Dataset Structure
```
HinglishMemeX/
  ├── images/
  │     ├── 000001.jpg
  │     ├── 000002.jpg
  │     └── ...
  ├── metadata.csv
  ├── README.md
```

### Each metadata entry contains:
- `id`: unique ID for the meme
- `image`: path or URL to the meme image
- `ocr_text_hinglish`: OCR text extracted from the meme (Hinglish)
- `claim_en`: distilled factual English claim
- `evidence_url`: link to fact-checking source
- `label`: one of `real`, `fake`, `satire`, `partially_true`
- `source`: origin (AltNews, Factly, BOOMLive, satire pages, etc.)
- `split`: train / validation / test

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## Tasks Supported
- **Multimodal misinformation detection** (4-way classification)
- **Satire detection**
- **Claim verification** via external evidence
- **OCR-based text understanding in code-mixed Hinglish**
- **Retrieval-Augmented Generation (RAG)** over evidence URLs

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## Dataset Statistics
- **Total memes:** 1,370
- **Classes:** Real, Fake, Satire, Partially True
- **Splits:**
  - Train: ~70%
  - Validation: ~10%
  - Test: ~20%

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## Data Collection & Curation
- Images collected from **public fact-checking portals** (AltNews, BOOMLive, Factly) and **popular social media satire pages**.
- Hinglish text was extracted using **EasyOCR** and **Google Vision API**, followed by light manual correction.
- Claims were distilled into short English factual statements.
- Evidence URLs were added for transparency and retrieval.
- Double annotator labeling with adjudication for disagreements.

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## Benchmarks & Baselines
Baseline experiments were conducted using:
- **CLIP ViT-L/14** (vision-only)
- **CLIP + IndicBERT** (late fusion)
- **Cross-attention dual encoders** (deep fusion)

Evaluation metrics: **macro-F1**, **accuracy**, **per-class F1**.

Satire and partially-true memes are particularly challenging due to semantic overlap.

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## Ethical Considerations
- Contains politically sensitive content; models trained on this dataset may inherit biases.
- Some memes may include misinformation or sensitive themes—handle responsibly.
- Recommended to use the dataset for **research only**.
- Provide confidence scores and retrieved evidence when deploying models.

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## ⚠️ Limitations
- Focused on Indian context → may not generalize globally.
- Natural OCR errors remain in some samples.
- Subjective boundaries between satire and partially-true content.

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## Loading the Dataset (Hugging Face)
```python
from datasets import load_dataset

dataset = load_dataset("pushkarsharma/HinglishMemeX")

def add_path(example):
    example["image"] = f"images/{example['id']}.jpg"
    return example

dataset = dataset.map(add_path)
```

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## License
Please refer to the **LICENSE** file for dataset licensing details.
Choose a license such as **CC BY 4.0** or **CC BY-SA 4.0** depending on your redistribution permissions.

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## Citation
If you use HinglishMemeX, please cite:
```
@misc{hinglishmemex2025,
  title = {HinglishMemeX: A Code-Mixed Multimodal Dataset for Misinformation and Satire Detection in Indian Memes},
  author = {Sharma, Pushkar},
  year = {2025},
  institution = {Indian Institute of Technology Patna}
}
```

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## Contact
**Maintainer:** Pushkar Sharma  
Email: *pushkarrokhel@gmail.com*


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Thank you for using HinglishMemeX! 

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license: apache-2.0
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