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.

中文翻译：

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用于分析新闻媒体极化的细微帧的弱监督学习

在本文中，我们提出了一种最低限度监督的方法，用于识别政治分歧主题的新闻报道中的细微框架。我们建议将 Boydstun 等人在 2014 年提出的广泛政策框架分解为细粒度的子框架，以更好地捕捉政治意识形态的差异。我们评估了建议的子框架及其嵌入，使用最少的监督学习，涉及三个主题，即移民、枪支管制和堕胎。我们展示了子框架在新闻媒体中捕捉意识形态差异和分析政治话语的能力。