---
title: "POLITICS: Pretraining with Same-story Article — Liu 2022"
description: "Measuring political ideology from text is central to political science, yet no general tool works reliably across text genres."
authors: ["Yujian Liu", "Xinliang Frederick Zhang", "David Wegsman", "Nick Beauchamp", "Lu Wang"]
year: 2022
venue: "Findings of the Association for Computational Linguistics: NAACL 2022"
source: "https://aclanthology.org/2022.findings-naacl.101/"
doi: "10.18653/v1/2022.findings-naacl.101"
arxiv: "2205.00619"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/liu-2022-politics-pretraining-same-story-article/"
---
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# POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection

**Authors:** Yujian Liu, Xinliang Frederick Zhang, David Wegsman, Nick Beauchamp, [Lu Wang](https://md.research.elamur.ai/free/authors/lu-wang/) · **Year:** 2022 · **Venue:** Findings of the Association for Computational Linguistics: NAACL 2022 · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.findings-naacl.101/) · [PDF](https://aclanthology.org/2022.findings-naacl.101.pdf) · [DOI 10.18653/v1/2022.findings-naacl.101](https://doi.org/10.18653/v1/2022.findings-naacl.101) · [arXiv 2205.00619](https://arxiv.org/abs/2205.00619) · [OpenAlex](https://openalex.org/W4225418906)

**Citations:** 51 citations (openalex-snapshot · 2026-10-14) · [19](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Measuring political ideology from text is central to political science, yet no general tool works reliably across text genres. Earlier models mostly relied on single-domain labeled data, and standard pretrained language models tend to capture local word-level cues. They largely miss how ideology appears in which events and entities an outlet chooses to report.

The authors built BIGNEWS, a corpus of US political news crawled from 11 outlets spanning left, center and right. From it they derived a version balanced by ideology and a version that groups same-story articles across outlets. They then continued pretraining RoBERTa into POLITICS using three objectives:
- a triplet-style ideology loss that pulls same-leaning coverage of a story together and pushes opposing coverage apart;
- a story loss that discourages shortcuts such as outlet boilerplate;
- masked language modeling that targets entities and sentiment words.

Across 11 ideology and stance tasks, POLITICS beats a strong SVM and earlier PLMs on most of them. Gains are largest on long, formal texts, and the model holds up well with little training data. The model and data give researchers a reusable tool for ideology analysis.

## Key points
- BIGNEWS has 3,689,229 English US-politics articles from 11 media outlets, collected via Common Crawl and Internet Archive (Jan 2000 – Jun 2021).
- BIGNEWSBLN is the ideology-balanced subset with 2,331,552 articles. BIGNEWSALIGN contains 1,060,512 stories with aligned cross-outlet articles.
- The evaluation covers 11 tasks on 8 datasets, including a new AllSides dataset. POLITICS wins on 8 of the 11 tasks.
- On long documents, POLITICS improves on RoBERTa by about 10% for both ideology prediction and stance detection.
- Code and data are available at github.com/launchnlp/POLITICS.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

## Cited by

Cited by

19 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 18 not yet.

- [StanceEval 2024: The First Arabic Stance Detection Shared Task](https://md.research.elamur.ai/free/papers/alturayeif-2024-stanceeval-2024-first-arabic-stance/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{liu2022politics,
  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, Nick and Wang, Lu},
  booktitle = {Findings of the Association for Computational Linguistics: NAACL 2022},
  year = {2022},
  doi = {10.18653/v1/2022.findings-naacl.101},
  eprint = {2205.00619},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2022.findings-naacl.101}
}
```

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