  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 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, we represent the ideology of a news source on a topic by
corpus-contextualized topic embedding utilizing a language model that has been
finetuned on recognizing partisanship of the news articles, and measure the
polarization between sources using cosine similarity. We apply our method to a
corpus of news about COVID-19 pandemic. Extensive experiments on different news
sources and topics demonstrate the effectiveness of our method to precisely
capture the topical polarization and alignment between different news sources.
To help clarify and validate results, we explain the polarization using the
Moral Foundation Theory.
