论文标题
分析不同层次的新闻文章中的政治偏见和不公平性
Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity
论文作者
论文摘要
媒体组织对我们社会的塑造信念和立场的影响很大，因此具有很大的重新评价。任何形式的媒体都可以包含过度偏见的内容，例如，以选择性或不完整的方式报告政治事件。因此，一个相关的问题是，这种不平衡的新闻报道是否会暴露出来。本文介绍的研究不仅涉及偏见的自动检测，而且进一步探讨了政治偏见和不公平的语言表现。在这方面，我们利用了6964篇新闻文章的新语料库，其标签来自Adfontesmedia.com，并开发了一种用于偏见评估的神经模型。通过在文章摘录上分析此模型，我们发现了不同级别的文本粒度级别的有见地的偏见模式，从单个单词到整个文章话语。
Media organizations bear great reponsibility because of their considerable influence on shaping beliefs and positions of our society. Any form of media can contain overly biased content, e.g., by reporting on political events in a selective or incomplete manner. A relevant question hence is whether and how such form of imbalanced news coverage can be exposed. The research presented in this paper addresses not only the automatic detection of bias but goes one step further in that it explores how political bias and unfairness are manifested linguistically. In this regard we utilize a new corpus of 6964 news articles with labels derived from adfontesmedia.com and develop a neural model for bias assessment. By analyzing this model on article excerpts, we find insightful bias patterns at different levels of text granularity, from single words to the whole article discourse.
