Sentence-level Media Bias Analysis Informed by Discourse Structures
Sentence-level Media Bias Analysis Informed by Discourse Structures
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DOI：
10.18653/v1/2022.emnlp-main.682
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发表时间：
2022
影响因子：
4.6
通讯作者：
Yuanyuan Lei;Ruihong Huang;Lu Wang;Nick Beauchamp
中科院分区：
文献类型：
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作者：
Yuanyuan Lei;Ruihong Huang;Lu Wang;Nick Beauchamp
随着公众和新闻媒体之间的两极分化继续加剧，越来越多的注意力被用于检测媒体偏见。然而，NLP社区最近的工作在单个文章的水平上识别出了偏见。然而，每一条本身都包含多个句子，这些句子的意识形态偏见各不相同。在本文中，我们的目标是确定文章中的句子，可以说明和解释整篇文章的整体偏见。我们发现，理解一个句子在讲述新闻故事中的话语作用，以及它与附近句子的关系，可以揭示作者的意识形态倾向，即使句子本身似乎只是中性的。特别地，我们考虑使用功能性新闻语篇结构和PDTB语篇关系来通知偏见句子识别，并从两种类型的语篇结构中提取辅助知识到我们的偏见句子识别系统中。在基准数据集上的实验结果表明，结合全局功能语篇结构和局部修辞语篇关系，可以有效提高偏见句识别的召回率8.27%~ 8.62%，准确率2.82%~ 3.48%.
As polarization continues to rise among both the public and the news media, increasing attention has been devoted to detecting media bias. Most recent work in the NLP community, however, identify bias at the level of individual articles. However, each article itself comprises multiple sentences, which vary in their ideological bias. In this paper, we aim to identify sentences within an article that can illuminate and explain the overall bias of the entire article. We show that understanding the discourse role of a sentence in telling a news story, as well as its relation with nearby sentences, can reveal the ideological leanings of an author even when the sentence itself appears merely neutral. In particular, we consider using a functional news discourse structure and PDTB discourse relations to inform bias sentence identification, and distill the auxiliary knowledge from the two types of discourse structure into our bias sentence identification system. Experimental results on benchmark datasets show that incorporating both the global functional discourse structure and local rhetorical discourse relations can effectively increase the recall of bias sentence identification by 8.27% - 8.62%, as well as increase the precision by 2.82% - 3.48%.