Companion repo for "Maximizing Neutrality in News Ordering" paper.
- Clone the repo
- Create a virtual environment using e.g., venv or Conda
- Install any missing packages using e.g., pip or Conda
- main packages are fairly standard (e.g., NetworkX, NumPy, SciPy, Matplotlib)
- Run the desired notebook using Jupyter Notebook or JupyterLab
generate_labeling_tasks.ipynb: Given a set of news headlines, generates pairs of headlines to annotate.labels_to_graph.ipynb: Reads labeled data and converts it into graph format. Also contains code to fit data to statistical distribution.detection.ipynb: Given a dataset, determines whether ordering was cherry-picked.maximizing_neutrality.ipynb: Given synthetic or semi-synthetic dataset, finds orderings that approximately maximize neutrality.
This project is licensed under the MIT License — see the LICENSE.md file for details.
