  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.
