---
license: mit
language: en
tags:
- text-classification
- distilbert
- news-framing
- conflict-detection
- indian-elections
- computational-social-science
- websci2025
datasets:
- custom
model-index:
- name: Framing the Fray - Conflict Frame Classifier
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      type: custom
      name: Indian Election News Headlines
    metrics:
    - type: f1
      value: 0.87
      name: Macro F1-Score
      args:
        average: macro
---

# Conflict Frame Classifier for Indian Election News

This is a `distilbert-base-uncased-finetuned-sst-2-english` model fine-tuned to classify news headlines into one of two frames: **conflict** or **non-conflict** (miscellaneous).

This model is the official artifact for the research paper:

**"Framing the Fray: Conflict Framing in Indian Election News Coverage"** accepted at the **17th ACM Web Science Conference (WebSci '25)**.

## Citation

If you use this model or the associated code, please cite our paper:

```bibtex
@inproceedings{chebrolu2025framing,
  title={Framing the Fray: Conflict Framing in Indian Election News Coverage},
  author={Chebrolu, Tejasvi and Chowdhary, Rohan and Vardhan, N Harsha and Kumaraguru, Ponnurangam and Rajadesingan, Ashwin},
  booktitle={Proceedings of the 17th ACM Web Science Conference 2025 (Websci '25)},
  year={2025},
  month={May},
  address={New Brunswick, NJ, USA},
  publisher={ACM},
  doi={10.1145/3717867.3717900}
}
```

## How to Use
You can use this model directly with the ``pipeline`` function from the `transformers` library:

```python
from transformers import pipeline

# Replace with your actual model repo ID after uploading
classifier = pipeline("text-classification", model="tejasvichebrolu/conflict-frame-classifier")

headlines = [
    "Days Before Polls, Kamal Haasan Meets Mamata Banerjee In Kolkata",
    "SP-BSP alliance led to wave of happiness; BJP worried, says Akhilesh Yadav",
    "Narendra Modi invoking Army for votes, says Tejashwi Yadav"
]

results = classifier(headlines)
for result in results:
    print(f"Label: {result['label']}, Score: {result['score']:.4f}")
```

## Training Procedure

The model was fine-tuned on a dataset of **860 headlines** annotated for the presence of a *conflict frame*.

---

## Hyperparameters

| Hyperparameter            | Value         |
|---------------------------|---------------|
| Conflict Class Weight     | 1.69          |
| Non-Conflict Class Weight | 9.01          |
| Learning Rate             | 6.008 × 10⁻⁵  |
| Epochs                    | 9             |

---

## Evaluation Results

The model's performance was evaluated using **5-fold cross-validation**. The average metrics are reported below:

| Class         | Precision | Recall | F1-Score |
|---------------|-----------|--------|----------|
| Conflict      | 0.92      | 0.91   | 0.92     |
| Non-Conflict  | 0.81      | 0.82   | 0.81     |
| Macro avg | 0.87  | 0.87 |   **0.87**       |