跨越党派界限：揭示新闻报道中的党派性与反党派性事件
Crossing the Aisle: Unveiling Partisan and Counter-Partisan Events in News Reporting
AI总结：
本文提出检测新闻报道中党派性与反党派性事件的任务，构建包含304篇文章、8,511条标注的PAC数据集并开展基准测试，揭示媒体通过事件取舍影响舆论的机制及大语言模型理解事件语境的需求。
摘要：
新闻媒体被期望坚持公正报道，但它们仍可能通过有选择地纳入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。先前在自然语言处理（NLP）领域的工作仅通过语言风格和用词来研究媒体偏见。在本文中，我们研究媒体在多大程度上平衡新闻报道，并通过事件的纳入或省略来影响消费者。我们首先引入检测党派性和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，其中包含来自意识形态多元媒体机构的 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，以凸显该任务的挑战。我们的发现既揭示了新闻以微妙方式塑造舆论的途径，也表明需要能够更好地在更广泛语境中理解事件的大语言模型。我们的数据集可在 https://github.com/launchnlp/Partisan-Event-Dataset 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
