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--- |
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license: mit |
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language: en |
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tags: |
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- text-classification |
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- distilbert |
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- news-framing |
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- conflict-detection |
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- indian-elections |
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- computational-social-science |
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- websci2025 |
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datasets: |
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- custom |
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model-index: |
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- name: Framing the Fray - Conflict Frame Classifier |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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type: custom |
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name: Indian Election News Headlines |
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metrics: |
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- type: f1 |
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value: 0.87 |
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name: Macro F1-Score |
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args: |
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average: macro |
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--- |
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# Conflict Frame Classifier for Indian Election News |
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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). |
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This model is the official artifact for the research paper: |
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**"Framing the Fray: Conflict Framing in Indian Election News Coverage"** accepted at the **17th ACM Web Science Conference (WebSci '25)**. |
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## Citation |
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If you use this model or the associated code, please cite our paper: |
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```bibtex |
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@inproceedings{chebrolu2025framing, |
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title={Framing the Fray: Conflict Framing in Indian Election News Coverage}, |
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author={Chebrolu, Tejasvi and Chowdhary, Rohan and Vardhan, N Harsha and Kumaraguru, Ponnurangam and Rajadesingan, Ashwin}, |
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booktitle={Proceedings of the 17th ACM Web Science Conference 2025 (Websci '25)}, |
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year={2025}, |
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month={May}, |
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address={New Brunswick, NJ, USA}, |
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publisher={ACM}, |
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doi={10.1145/3717867.3717900} |
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} |
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``` |
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## How to Use |
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You can use this model directly with the ``pipeline`` function from the `transformers` library: |
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```python |
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from transformers import pipeline |
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# Replace with your actual model repo ID after uploading |
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classifier = pipeline("text-classification", model="tejasvichebrolu/conflict-frame-classifier") |
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headlines = [ |
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"Days Before Polls, Kamal Haasan Meets Mamata Banerjee In Kolkata", |
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"SP-BSP alliance led to wave of happiness; BJP worried, says Akhilesh Yadav", |
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"Narendra Modi invoking Army for votes, says Tejashwi Yadav" |
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] |
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results = classifier(headlines) |
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for result in results: |
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print(f"Label: {result['label']}, Score: {result['score']:.4f}") |
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``` |
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## Training Procedure |
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The model was fine-tuned on a dataset of **860 headlines** annotated for the presence of a *conflict frame*. |
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--- |
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## Hyperparameters |
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| Hyperparameter | Value | |
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|---------------------------|---------------| |
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| Conflict Class Weight | 1.69 | |
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| Non-Conflict Class Weight | 9.01 | |
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| Learning Rate | 6.008 × 10⁻⁵ | |
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| Epochs | 9 | |
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--- |
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## Evaluation Results |
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The model's performance was evaluated using **5-fold cross-validation**. The average metrics are reported below: |
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| Class | Precision | Recall | F1-Score | |
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|---------------|-----------|--------|----------| |
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| Conflict | 0.92 | 0.91 | 0.92 | |
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| Non-Conflict | 0.81 | 0.82 | 0.81 | |
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| Macro avg | 0.87 | 0.87 | **0.87** | |
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