提出联合识别新闻中遗漏、夸大和信源偏倚的方法。
What's in the News? Towards Identification of Bias by Commission, Omission, and Source Selection (COSS)
- 将三种偏倚统一建模为联合任务，提升识别全面性。
- 通过文本复用模式提取特征，实现偏倚可视化分析。
- 适合媒体审核与信息可信度评估场景使用。
在信息过载的当下，判断新闻来源可靠性及内容中立性对读者构成挑战。本文提出一种自动识别偏倚的联合方法，涵盖遗漏、夸大和信源选择（COSS）三类问题，突破以往分别处理的局限。基于流程化框架，明确各步骤目标与任务，并展示如何利用提取的文本复用特征与模式进行偏倚可视化分析，为新闻可信度评估提供新思路。
原文摘要 · Abstract (English)
In a world overwhelmed with news, determining which information comes from reliable sources or how neutral is the reported information in the news articles poses a challenge to news readers. In this paper, we propose a methodology for automatically identifying bias by commission, omission, and source selection (COSS) as a joint three-fold objective, as opposed to the previous work separately addressing these types of bias. In a pipeline concept, we describe the goals and tasks of its steps toward bias identification and provide an example of a visualization that leverages the extracted features and patterns of text reuse.
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