With the increasing pursuit of objective reports, automatically understanding media bias has drawn more attention in recent research. However, most of the previous work examines media bias from Western ideology, such as the left and right in the political spectrum, which is not applicable to Chinese outlets. Based on the previous lexical bias and informational bias structure, we refine it from the Chinese perspective and go one step further to craft data with 7 fine-grained labels. To be specific, we first construct a dataset with Chinese news reports about COVID-19 which is annotated by our newly designed system, and then conduct substantial experiments on it to detect media bias. However, the scale of the annotated data is not enough for the latest deep-learning technology, and the cost of human annotation in media bias, which needs a lot of professional knowledge, is too expensive. Thus, we explore some context enrichment methods to automatically improve these problems. In Data-Augmented Context Enrichment (DACE), we enlarge the training data; while in Retrieval-Augmented Context Enrichment (RACE), we improve information retrieval methods to select valuable information and integrate it into our models to better understand bias. Extensive experiments are conducted on both our dataset and an English dataset BASIL. Our results show that both methods outperform our baselines, while the RACE methods are more efficient and have more potential.

翻译：随着对客观报道的日益追求，自动理解媒体偏见在近期的研究中引起了更多关注。然而，以往的大多数研究从西方意识形态视角（如政治光谱中的左派和右派）考察媒体偏见，这并不适用于中国媒体机构。基于先前关于词汇偏见和信息偏见的结构框架，我们从中国视角对其进行了细化，并进一步构建了包含7个细粒度标签的数据集。具体而言，我们首先构建了一个关于COVID-19的中文新闻报道数据集，该数据集由我们新设计的标注系统进行标注，并在此基础上开展了大量实验来检测媒体偏见。然而，标注数据的规模不足以支持最新的深度学习技术，且媒体偏见领域中的人工标注成本高昂，因为其需要大量专业知识。因此，我们探索了一些上下文丰富方法来自动改善这些问题。在数据增强的上下文丰富方法（DACE）中，我们扩大了训练数据；而在检索增强的上下文丰富方法（RACE）中，我们改进了信息检索方法以选择有价值的信息，并将其整合到模型中，从而更好地理解偏见。我们在自建数据集和英文数据集BASIL上进行了大量实验。结果表明，两种方法均优于基线方法，而RACE方法效率更高且更具潜力。