Slap4slip

This repository contains the code for the NAACL 2022 Findings paper Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity.

Dependencies

The code requires Python>=3.6, numpy>=1.18, torch>=1.2, and torch_geometric>=1.6.

Usage

To replicate the experiments using SF-SGAE, S-SGAE, and F-SGAE, run the script src/train.sh. To replicate the experiment using SF-SLAE, run the script src/train_linear.sh. To replicate the experiment using SF-GAE, run the script src/train_nonsparse.sh.

Citation

If you use the code in this repository, please cite the following paper:

@inproceedings{hofmann2022slap4slip,
    title = {Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity},
    author = {Hofmann, Valentin and Dong, Xiaowen and Pierrehumbert, Janet and Sch{\"u}tze, Hinrich},
    booktitle = {Findings of the Association for Computational Linguistics: NAACL 2022},
    year = {2022}
}