---
language:
- en
tags:
- politics
- roberta
license:
- cc-by-nc-sa-4.0
---

## POLITICS
POLITICS, a pretrained model on English news articles of politics, is produced via continued training on RoBERTa, based on a **P**retraining **O**bjective **L**everaging **I**nter-article **T**riplet-loss using **I**deological **C**ontent and **S**tory. 

**ALERT:** POLITICS is a pre-trained **language model** that specializes in comprehending news articles and understanding ideological content. However, POLITICS cannot be used **out-of-the-box** on downstream tasks such as predicting ideological leanings and discerning stances expressed in texts. To perform predictions on downstream tasks, you are advised to **fine-tune** POLITICS on your own dataset first.

Details of our proposed training objectives (i.e., Ideology-driven Pretraining Objectives) and experimental results of POLITICS can be found in our NAACL-2022 Findings [paper](https://aclanthology.org/2022.findings-naacl.101.pdf) and GitHub [Repo](https://github.com/launchnlp/POLITICS).

Together with POLITICS, we also release our curated large-scale dataset (i.e., BIGNEWS) for pretraining, consisting of more than 3.6M political news articles. This asset can be requested [here](https://docs.google.com/forms/d/e/1FAIpQLSf4hft2AHbuak8jHcltVec_2HviaBBVKXPN4OC-CuW4OFORsw/viewform).

## Citation
Please cite our paper if you use the **POLITICS** model:
```
@inproceedings{liu-etal-2022-POLITICS,
    title = "POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection",
    author = "Liu, Yujian and
    Zhang, Xinliang Frederick and
    Wegsman, David and
    Beauchamp, Nicholas and 
    Wang, Lu"
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
    year = "2022",
```