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：随着客观报道需求的日益增长，自动理解媒体偏见已成为近期的研究热点。然而，以往研究多从西方意识形态视角（如政治光谱中的左翼与右翼）考察媒体偏见，这并不适用于中国媒体。基于既有词汇偏见与信息偏见框架，我们从中文视角对其进行细化，并进一步构建了包含7个细粒度标签的数据集。具体而言，我们首先利用新设计的标注系统，构建了一个关于新冠疫情中文新闻报道的数据集，并在此基础上开展大量实验以检测媒体偏见。然而，受限于标注数据规模不足以及媒体偏见领域人力标注成本高昂（需大量专业知识），现有数据难以支撑最新深度学习技术。为此，我们探索了多种上下文自动丰富方法以改善上述问题。在数据增强上下文丰富方法（DACE）中，我们扩大了训练数据规模；而在检索增强上下文丰富方法（RACE）中，我们改进了信息检索方法，以筛选有价值信息并整合至模型中，从而提升对偏见的理解能力。我们在中文数据集及英文数据集BASIL上开展了广泛实验，结果表明，两种方法均优于基线模型，其中RACE方法效率更高且更具潜力。