Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media (2020)

Abstract: In this paper, we suggest a minimally supervised approach for identifying nuanced frames in news article coverage of politically divisive topics. We suggest to break the broad policy frames suggested by Boydstun et al., 2014 into fine-grained subframes which can capture differences in political ideology in a better way. We evaluate the suggested subframes and their embedding, learned using minimal supervision, over three topics, namely, immigration, gun-control, and abortion. We demonstrate the ability of the subframes to capture ideological differences and analyze political discourse in news media.

## introduction as the political climate and the news media in the united states become increasingly polarized @xcite , it is important to understand the perspectives underlying the political divisions and analyze their differences. political framing, studied by political scientists @xcite , provides the means to study these perspectives. it is a nuanced political strategy, used to bias the discussion on an issue towards a specific stance by emphasizing specific aspects that prime the reader to accept that stance. to help clarify this definition, consider two articles on the highly polarized immigration issue. ## related work understanding and analyzing political perspectives in news coverage has gathered significant interest in recent years @xcite @xcite @xcite @xcite , broadly related to analysis of bias or parti-sanship and expressions of implicit sentiment @xcite @xcite @xcite @xcite . in addition to predicting the underlying perspective, our work focuses on explaining the perspectives underlying the ideological coverage of news events. we build specifically on issue-frames @xcite , however our work is related to framing and agenda setting analysis work more broadly @xcite @xcite @xcite . ## data collection we collected 21, 645 news articles on three politically polarized topics, abortion, immigration and gun control. we used the hyper-partisan news dataset @xcite and we crawled additional news articles on the topics from sources with known political bias provided by mediabiasfactcheck.com, where the articles are categorized based on their topics on the websites of the sources. all the news articles are u.s. politics based and written in english. we identify the topic of news articles in the hyper-partisan news dataset by looking at presence of certain keywords in the titles and urls of news articles. for example, 'abortion' for topic abortion; 'migrant', 'migration' for topic immigration; 'gun' for topic gun control. in case of absence of any of the keywords in the title or url, we annotate the article with the corresponding topic if the keywords appear at least 3 times in the article text. the hyper-partisan news dataset provides bias-labels of the news articles and we labeled our crawled news articles based on their source bias according to mediabiasfactcheck.com. we consider only the left and right biased news articles. the dataset is summarized in ## modeling political framing our goal in this paper is to identify framing dimensions that can be used to capture difference between the two ideological polarities. the frames used for this analysis could be issue specific, or as suggested by @xcite , generalize over several policy issues, using a fixed set of framing dimensions. in many cases, as we show in this paper, the general policy frames do not capture the nuanced aspects of the issue highlighted by each side to bias the discussion. in example 1, both sides use the economy frame, however it is instantiated in different ways, to promote opposite views. to help combat this issue, we suggest a middle ground between generalized policy frames and event-specific frames, by constructing a hierarchy of frames and sub-frames, the first derived from the definitions and data of media frames corpus @xcite , and the second emerging from the data directly, by tracking the differences in the vocabulary used when these frames are instantiated in different topics, and grouping them to sub-frames. this process takes place in three steps. first, we create a lexicon of topic specific phrases capturing how the frame is invoked in each policy issue. second, we manually group these phrases into subframes. finally, we embed the sub-frames using weak-supervision, allowing us to associate subframes with new text, beyond the extracted phrases. the following subsections explain each step. ## analyzing polarization on news media in this section we show how our model can be used to analyze polarization on news media. we focus on comparing several different qualitative results, contrasting the analysis obtained by policy frames and our subframe approach. similar to the previous section, we identify the top 3 subframes in a news article using the embedding model. to compare with frame usage, we identify the top 3 frames for each news article by following the same process used by @xcite , counting the number of word occurrences in an article from a frame lexicon and taking the most frequent 3 as predicted frames. ## summary we study the news media coverage of 3 politically polarized topics -abortion, immigration, and gun control; by breaking the high level policy frames into more fine grained, and topic-specific, subframes. we demonstrate that the subframes can 2 pmi calculation details is in appendix e.