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README.md

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+## 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. 
+
+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://arxiv.org/pdf/2205.00619.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",
+```