Detecting Polarized Topics Using Partisanship-aware Contextualized Topic Embeddings

Zihao He, Negar Mokhberian, António Câmara, Andres Abeliuk, Kristina Lerman


Abstract
Growing polarization of the news media has been blamed for fanning disagreement, controversy and even violence. Early identification of polarized topics is thus an urgent matter that can help mitigate conflict. However, accurate measurement of topic-wise polarization is still an open research challenge. To address this gap, we propose Partisanship-aware Contextualized Topic Embeddings (PaCTE), a method to automatically detect polarized topics from partisan news sources. Specifically, utilizing a language model that has been finetuned on recognizing partisanship of the news articles, we represent the ideology of a news corpus on a topic by corpus-contextualized topic embedding and measure the polarization using cosine distance. We apply our method to a dataset of news articles about the COVID-19 pandemic. Extensive experiments on different news sources and topics demonstrate the efficacy of our method to capture topical polarization, as indicated by its effectiveness of retrieving the most polarized topics.
Anthology ID:
2021.findings-emnlp.181
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2102–2118
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.181
DOI:
10.18653/v1/2021.findings-emnlp.181
Bibkey:
Cite (ACL):
Zihao He, Negar Mokhberian, António Câmara, Andres Abeliuk, and Kristina Lerman. 2021. Detecting Polarized Topics Using Partisanship-aware Contextualized Topic Embeddings. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2102–2118, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Detecting Polarized Topics Using Partisanship-aware Contextualized Topic Embeddings (He et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.181.pdf
Video:
 https://aclanthology.org/2021.findings-emnlp.181.mp4
Code
 zaghe568/pacte-polarized-topics-detection