论文标题
细微差别的框架的弱监督学习，用于分析新闻媒体的两极分化
Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media
论文作者
论文摘要
在本文中，我们建议在新闻文章的政治分裂主题报道中进行最少监督的方法，以识别细微差别的框架。我们建议将2014年Boydstun等人建议的广泛政策框架分解为细粒度的子框架，这些子框架可以更好地以更好的方式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，并使用最小的监督学到了三个主题，即移民，枪支控制和流产。我们证明了子框架捕获意识形态差异并分析新闻媒体中的政治话语的能力。
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.
