Informational bias is bias conveyed through sentences or clauses that provide tangential, speculative or background information that can sway readers' opinions towards entities. By nature, informational bias is context-dependent, but previous work on informational bias detection has not explored the role of context beyond the sentence. In this paper, we explore four kinds of context for informational bias in English news articles: neighboring sentences, the full article, articles on the same event from other news publishers, and articles from the same domain (but potentially different events). We find that integrating event context improves classification performance over a very strong baseline. In addition, we perform the first error analysis of models on this task. We find that the best-performing context-inclusive model outperforms the baseline on longer sentences, and sentences from politically centrist articles.
翻译:信息偏差是通过提供不相干、投机或背景信息的句子或条款表达的偏见,这些句子或条款可以使读者对实体的看法产生偏差。从性质上讲,信息偏差取决于背景,但先前关于信息偏差探测的工作并未探讨超出句子的范围。在本文中,我们探讨了英文新闻文章中信息偏差的四种背景:邻近句子、整篇文章、其他新闻出版商关于同一事件的文章以及来自同一领域的文章(但可能不同事件)。我们发现,综合事件背景会改善分类工作,超越一个非常强的基线。此外,我们对这项任务的模型进行第一次错误分析。我们发现,最佳的环境包容性模式超越了较长刑期的基线,也超越了政治中心文章的句子。