POLUSA数据集：90万篇政治新闻文章，按时间和出口受欢迎程度平衡
The POLUSA Dataset: 0.9M Political News Articles Balanced by Time and Outlet Popularity
摘要：
报道政策问题的新闻文章是社会科学中的重要信息来源，也经常用于其他用例，例如，训练NLP语言模型。为了从新闻分析中获得有意义的见解，需要代表真实世界分布的大型数据集，例如，关于所包含的出口的受欢迎程度、局部或跨时间。经常需要关于媒体出版商政治倾向的信息，例如，研究政治领域新闻报道的差异，这是社会科学研究媒体偏见和相关社会问题时的主要用例之一。关于这些要求，现有的数据集存在重大缺陷，导致研究界在数据集创建方面的冗余和繁琐的工作。为了填补这一空白，我们提出了POLUSA，这是一个代表美国普通新闻消费者所感知的在线媒体格局的数据集。该数据集包含2017年1月至2019年8月期间由代表政治光谱的18家新闻媒体发表的90万篇文章。每一个出口都被贴上了政治倾向的标签，我们使用八个数据源的系统聚合来得出这一点。新闻数据集在出版日期和出口受欢迎程度方面是平衡的。POLUSA可以学习各种科目，例如，媒体效应和政治党派。由于其大小，数据集允许利用数据密集型深度学习方法。
英文摘要：
News articles covering policy issues are an essential source of information in the social sciences and are also frequently used for other use cases, e.g., to train NLP language models. To derive meaningful insights from the analysis of news, large datasets are required that represent real-world distributions, e.g., with respect to the contained outlets' popularity, topically, or across time. Information on the political leanings of media publishers is often needed, e.g., to study differences in news reporting across the political spectrum, which is one of the prime use cases in the social sciences when studying media bias and related societal issues. Concerning these requirements, existing datasets have major flaws, resulting in redundant and cumbersome effort in the research community for dataset creation. To fill this gap, we present POLUSA, a dataset that represents the online media landscape as perceived by an average US news consumer. The dataset contains 0.9M articles covering policy topics published between Jan. 2017 and Aug. 2019 by 18 news outlets representing the political spectrum. Each outlet is labeled by its political leaning, which we derive using a systematic aggregation of eight data sources. The news dataset is balanced with respect to publication date and outlet popularity. POLUSA enables studying a variety of subjects, e.g., media effects and political partisanship. Due to its size, the dataset allows to utilize data-intense deep learning methods.
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。
