Detecting Polarized Topics Using Partisanship-aware Contextualized Topic Embeddings (2021)

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.

## introduction the media environment has grown increasingly polarized in recent years, creating social, cultural and political divisions @xcite . although a diversity of opinions is healthy, and even necessary for democratic discourse, unchecked polarization can paralyze society by suppressing consensus required for effective governance @xcite . in more extreme cases, polarization leads to disagreement, conflict and even violence. the covid-19 pandemic has exposed many of our vulnerabilities to the pernicious effects of polarization. public opinions about covid-19 @xcite , as well as messaging by political elites (green et al., 2020; bhanot 1 code and data are publicly available at https://github.com/zaghe568/ pacte-polarized-topics-detection . and hopkins, 2020), are sharply divided along partisan lines. according to a pew report @xcite , partisanship significantly explains attitudes about the costs and benefits of various mitigation strategies, including non-pharmaceutical interventions and lockdowns, and even explains regional differences in the pandemic's toll in the us @xcite . in mass media a variety of topics is discussed every day, and polarization can form on different topics. therefore, identifying nascent disagreements and growing controversies of different topics in news media and public discourse would help journalists craft more balanced news coverage @xcite . different from previous works that study polarization from a more coarse-grained perspective, @xcite were the first to study polarized topics using tweets about 21 mass shootings to show that some topics were more polarized than others. however, their approach to represent semantic information with word frequencies is less expressive than modern methods allow. to better capture the topical polarization among partisan (liberal vs. conservative) media sources, we propose partisanship-aware contextualized topic embeddings (pacte). specifically, given a text corpus containing news articles from both sides, we first extract a set of topics utilizing lda topic modeling @xcite . next, we finetune a pretrained language model @xcite to recognize the partisanship of the news articles so as to render it partisanship-aware. then for each article, we represent its ideology on a topic by a vector, called document-contextualized 2 (dc) topic embedding, by aggregating language model representations of the topic keywords contextualized by the article. such a representation sheds light primarily on the tokens that appear in the topic keywords and thus concentrates on the topic-oriented local semantics in the context of the article, instead of the global semantics from the article that might contain irrelevant and noisy information. we further represent the ideology of the news corpus on the topic, what we call corpus-contextualized (cc) topic embedding, by aggregating the dc topic embeddings. as a result, the ideology of the news corpus on a topic is represented by a single vector. finally, we measure the polarization between two news sources on the topic using the cosine distance between such vectors. for evaluation, we create ground truth by annotating the polarization of pairs of partisan news sources on a variety of topics. we evaluate the topic polarization scores produced by pacte against the ground truth on the task of polarized topics retrieval. experiments on nine pairs of partisan news sources demonstrate that compared to baselines, pacte is more effective in capturing topic polarization and retrieving polarized topics. we argue that public media watchdogs and social media platforms can utilize such a simple-yet-effective tool to flag discussions that have grown divisive so that action could be taken to reduce partisan divisions and improve civil discourse. ## related work the partisan polarization in the us media is a widely studied topic @xcite . during the onset of the covid-19 pandemic, the polarization among the political elites and the news media causes a lot of confusion. for example, @xcite show that covid-19 media coverage is politicized and polarized. other works have been studying the polarization in media from different perspectives. focusing on the differences in the languages of liberals and conservatives, khudabukhsh et al. (2020 ## methodology the proposed pacte framework consists of four components: 1) lda topic modeling, 2) partisanship learning, 3) partisanship-aware contextualized topic embedding generation, and 4) measuring polarization and ranking topics. the overall framework is illustrated in figure 1 . in this section we elaborate on each component in detail.