README.md

---
language: en
license: apache-2.0
library_name: transformers
tags:
- text-classification
- personal-narrative
- political-discourse
- computational-social-science
- websci25
datasets:
- custom-reddit-dataset
base_model: falkne/storytelling-LM-europarl-mixed-en
---

# Personal Narrative Classifier (WebSci'25)

This is the official repository for the text classification model presented in the paper: **"Personal Narratives Empower Politically Disinclined Individuals to Engage in Political Discussions"**, which received a Best Paper Honorable Mention at the 17th ACM Web Science Conference (WebSci'25).

The model is a fine-tuned BERT-based classifier (`falkne/storytelling-LM-europarl-mixed-en`) designed to identify personal narratives in online comments.

## Model Description

This model classifies a given text as either a "Personal Narrative" or "Not a Personal Narrative". It was developed to support a large-scale computational analysis of how personal stories affect engagement in online political discussions on Reddit.

- **Label 0**: Not a Personal Narrative
- **Label 1**: Personal Narrative

## Intended Uses & Limitations

### Intended Use

This model is intended for researchers in computational social science, political science, communication, and HCI to study online discourse. It can be used to:
- Quantify the use of personal narratives in various online communities.
- Analyze the reception and impact of story-based arguments.
- Replicate and extend the findings of the original paper.

### Limitations

As noted in the paper, this model has several limitations:
- The training and evaluation data comes from political subreddits on Reddit from 2020-2021. Its performance may vary on other platforms or time periods.
- The definition of "political activity" was based on subreddit engagement, which may not capture all forms of political interest.
- The model does not analyze the content or veracity of the narratives. Personal narratives can also be used to spread misinformation, which is an avenue for future research.

## How to Use

You can use this model with the `transformers` library pipeline for easy inference.

```python
from transformers import pipeline

repo_id = "tejasvichebrolu/personal-narrative-classifier"
classifier = pipeline("text-classification", model=repo_id)

# Example texts
narrative_text = "I’m in Alabama and oh my god it was so humid yesterday. I was so unproductive from how bad it was."
non_narrative_text = "The most straightforward solution is to encourage others to engage with politics online."

# Get predictions
results = classifier([narrative_text, non_narrative_text])
for text, result in zip([narrative_text, non_narrative_text], results):
    print(f"Text: {text}")
    # The pipeline may return LABEL_0/LABEL_1 or the names from the config
    print(f"  -> Prediction: {result['label']}, Score: {result['score']:.4f}\n")
```

## Training and Evaluation

The model was fine-tuned on a dataset of 2,000 manually labeled Reddit comments. It achieved a macro average F1-score of **0.82** in 5-fold cross-validation. For more details on the training procedure and performance, please refer to the paper.

## Citation

If you use this model or its findings in your research, please cite our paper:

```bibtex
@inproceedings{chebrolu2025narratives,
  title={{Personal Narratives Empower Politically Disinclined Individuals to Engage in Political Discussions}},
  author={{Chebrolu, Tejasvi and Kumaraguru, Ponnurangam and Rajadesingan, Ashwin}},
  booktitle={{Proceedings of the 17th ACM Web Science Conference 2025 (Websci '25)}},
  year={{2025}},
  organization={{ACM}},
  doi={10.1145/3717867.3717899}
}
```