In Plain Sight: Media Bias Through the Lens of Factual Reporting
In Plain Sight: Media Bias Through the Lens of Factual Reporting
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显而易见：事实报道视角下的媒体偏见
DOI：
10.18653/v1/d19-1664
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发表时间：
2019
影响因子：
4.9
通讯作者：
Lu Wang
中科院分区：
文献类型：
--
作者：
Lisa Fan;M. White;Eva Sharma;Ruisi Su;Prafulla Kumar Choubey;Ruihong Huang;Lu Wang
新闻媒体中政治偏见的日益普遍，要求提高公众对它的认识，并采取强有力的检测方法。虽然NLP之前的工作主要集中在语言属性（如单词选择和语法）所捕获的词汇偏见，但其他类型的偏见源于文本中选择的实际内容。在这项工作中，我们调查的信息偏见的影响：事实的内容，但可以部署到动摇读者的意见。我们首先产生了一个新的数据集，BASIL，300篇新闻文章注释与1,727个偏见跨度，并找到证据表明，信息偏见出现在新闻文章中更频繁地比词汇偏见。我们进一步研究我们的注释，以观察不同媒体的新闻文章中信息偏见的表面。最后，通过对我们的标记数据进行BERT微调，提出了信息偏差预测的基线模型，指出了任务的挑战和未来的方向。
The increasing prevalence of political bias in news media calls for greater public awareness of it, as well as robust methods for its detection. While prior work in NLP has primarily focused on the lexical bias captured by linguistic attributes such as word choice and syntax, other types of bias stem from the actual content selected for inclusion in the text. In this work, we investigate the effects of informational bias: factual content that can nevertheless be deployed to sway reader opinion. We first produce a new dataset, BASIL, of 300 news articles annotated with 1,727 bias spans and find evidence that informational bias appears in news articles more frequently than lexical bias. We further study our annotations to observe how informational bias surfaces in news articles by different media outlets. Lastly, a baseline model for informational bias prediction is presented by fine-tuning BERT on our labeled data, indicating the challenges of the task and future directions.