@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",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.101",
doi = "10.18653/v1/2022.findings-naacl.101",
pages = "1354--1374",
abstract = "Ideology is at the core of political science research. Yet, there still does not exist general-purpose tools to characterize and predict ideology across different genres of text. To this end, we study Pretrained Language Models using novel ideology-driven pretraining objectives that rely on the comparison of articles on the same story written by media of different ideologies. We further collect a large-scale dataset, consisting of more than 3.6M political news articles, for pretraining. Our model POLITICS outperforms strong baselines and the previous state-of-the-art models on ideology prediction and stance detection tasks. Further analyses show that POLITICS is especially good at understanding long or formally written texts, and is also robust in few-shot learning scenarios.",
}
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<abstract>Ideology is at the core of political science research. Yet, there still does not exist general-purpose tools to characterize and predict ideology across different genres of text. To this end, we study Pretrained Language Models using novel ideology-driven pretraining objectives that rely on the comparison of articles on the same story written by media of different ideologies. We further collect a large-scale dataset, consisting of more than 3.6M political news articles, for pretraining. Our model POLITICS outperforms strong baselines and the previous state-of-the-art models on ideology prediction and stance detection tasks. Further analyses show that POLITICS is especially good at understanding long or formally written texts, and is also robust in few-shot learning scenarios.</abstract>
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%0 Conference Proceedings
%T POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection
%A Liu, Yujian
%A Zhang, Xinliang Frederick
%A Wegsman, David
%A Beauchamp, Nicholas
%A Wang, Lu
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F liu-etal-2022-politics
%X Ideology is at the core of political science research. Yet, there still does not exist general-purpose tools to characterize and predict ideology across different genres of text. To this end, we study Pretrained Language Models using novel ideology-driven pretraining objectives that rely on the comparison of articles on the same story written by media of different ideologies. We further collect a large-scale dataset, consisting of more than 3.6M political news articles, for pretraining. Our model POLITICS outperforms strong baselines and the previous state-of-the-art models on ideology prediction and stance detection tasks. Further analyses show that POLITICS is especially good at understanding long or formally written texts, and is also robust in few-shot learning scenarios.
%R 10.18653/v1/2022.findings-naacl.101
%U https://aclanthology.org/2022.findings-naacl.101
%U https://doi.org/10.18653/v1/2022.findings-naacl.101
%P 1354-1374
Markdown (Informal)
[POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection](https://aclanthology.org/2022.findings-naacl.101) (Liu et al., Findings 2022)
ACL