News media is expected to uphold unbiased reporting. Yet they may still affect public opinion by selectively including or omitting events that support or contradict their ideological positions. Prior work in NLP has only studied media bias via linguistic style and word usage. In this paper, we study to which degree media balances news reporting and affects consumers through event inclusion or omission. We first introduce the task of detecting both partisan and counter-partisan events: events that support or oppose the author's political ideology. To conduct our study, we annotate a high-quality dataset, PAC, containing 8,511 (counter-)partisan event annotations in 304 news articles from ideologically diverse media outlets. We benchmark PAC to highlight the challenges of this task. Our findings highlight both the ways in which the news subtly shapes opinion and the need for large language models that better understand events within a broader context. Our dataset can be found at https://github.com/launchnlp/Partisan-Event-Dataset.

翻译：新闻媒体被期望秉持公正的报道。然而，媒体仍可能通过选择性地包含或省略支持或反对其意识形态立场的事件来影响公众舆论。先前的自然语言处理研究仅通过语言风格和词汇使用来研究媒体偏见。在本文中，我们研究媒体在多大程度上通过事件的包含或省略来平衡新闻报道并影响受众。我们首先提出检测党派性与反党派性事件的任务：即支持或反对作者政治意识形态的事件。为开展研究，我们标注了一个高质量数据集PAC，其中包含来自不同意识形态媒体机构的304篇新闻文章中的8,511条（反）党派性事件标注。我们对PAC进行基准测试，以突出该任务的挑战性。我们的发现既揭示了新闻潜移默化塑造舆论的方式，也凸显了需要能更深入理解语境中事件的大型语言模型。我们的数据集可在https://github.com/launchnlp/Partisan-Event-Dataset获取。