@inproceedings{guo-etal-2022-capturing,
title = "Capturing Topic Framing via Masked Language Modeling",
author = "Guo, Xiaobo and
Ma, Weicheng and
Vosoughi, Soroush",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.507",
doi = "10.18653/v1/2022.findings-emnlp.507",
pages = "6811--6825",
abstract = "Differential framing of issues can lead to divergent world views on important issues. This is especially true in domains where the information presented can reach a large audience, such as traditional and social media. Scalable and reliable measurement of such differential framing is an important first step in addressing them. In this work, based on the intuition that framing affects the tone and word choices in written language, we propose a framework for modeling the differential framing of issues through masked token prediction via large-scale fine-tuned language models (LMs). Specifically, we explore three key factors for our framework: 1) prompt generation methods for the masked token prediction; 2) methods for normalizing the output of fine-tuned LMs; 3) robustness to the choice of pre-trained LMs used for fine-tuning. Through experiments on a dataset of articles from traditional media outlets covering five diverse and politically polarized topics, we show that our framework can capture differential framing of these topics with high reliability.",
}
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<abstract>Differential framing of issues can lead to divergent world views on important issues. This is especially true in domains where the information presented can reach a large audience, such as traditional and social media. Scalable and reliable measurement of such differential framing is an important first step in addressing them. In this work, based on the intuition that framing affects the tone and word choices in written language, we propose a framework for modeling the differential framing of issues through masked token prediction via large-scale fine-tuned language models (LMs). Specifically, we explore three key factors for our framework: 1) prompt generation methods for the masked token prediction; 2) methods for normalizing the output of fine-tuned LMs; 3) robustness to the choice of pre-trained LMs used for fine-tuning. Through experiments on a dataset of articles from traditional media outlets covering five diverse and politically polarized topics, we show that our framework can capture differential framing of these topics with high reliability.</abstract>
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%0 Conference Proceedings
%T Capturing Topic Framing via Masked Language Modeling
%A Guo, Xiaobo
%A Ma, Weicheng
%A Vosoughi, Soroush
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F guo-etal-2022-capturing
%X Differential framing of issues can lead to divergent world views on important issues. This is especially true in domains where the information presented can reach a large audience, such as traditional and social media. Scalable and reliable measurement of such differential framing is an important first step in addressing them. In this work, based on the intuition that framing affects the tone and word choices in written language, we propose a framework for modeling the differential framing of issues through masked token prediction via large-scale fine-tuned language models (LMs). Specifically, we explore three key factors for our framework: 1) prompt generation methods for the masked token prediction; 2) methods for normalizing the output of fine-tuned LMs; 3) robustness to the choice of pre-trained LMs used for fine-tuning. Through experiments on a dataset of articles from traditional media outlets covering five diverse and politically polarized topics, we show that our framework can capture differential framing of these topics with high reliability.
%R 10.18653/v1/2022.findings-emnlp.507
%U https://aclanthology.org/2022.findings-emnlp.507
%U https://doi.org/10.18653/v1/2022.findings-emnlp.507
%P 6811-6825
Markdown (Informal)
[Capturing Topic Framing via Masked Language Modeling](https://aclanthology.org/2022.findings-emnlp.507) (Guo et al., Findings 2022)
ACL
- Xiaobo Guo, Weicheng Ma, and Soroush Vosoughi. 2022. Capturing Topic Framing via Masked Language Modeling. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6811–6825, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.