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News articles covering policy issues are an essential source of informationin 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 analysisof news, large datasets are required that represent real-world distributions,e.g., with respect to the contained outlets' popularity, topically, or acrosstime. Information on the political leanings of media publishers is oftenneeded, e.g., to study differences in news reporting across the politicalspectrum, which is one of the prime use cases in the social sciences whenstudying media bias and related societal issues. Concerning these requirements,existing datasets have major flaws, resulting in redundant and cumbersomeeffort in the research community for dataset creation. To fill this gap, wepresent POLUSA, a dataset that represents the online media landscape asperceived by an average US news consumer. The dataset contains 0.9M articlescovering policy topics published between Jan. 2017 and Aug. 2019 by 18 newsoutlets representing the political spectrum. Each outlet is labeled by itspolitical leaning, which we derive using a systematic aggregation of eight datasources. The news dataset is balanced with respect to publication date andoutlet popularity. POLUSA enables studying a variety of subjects, e.g., mediaeffects and political partisanship. Due to its size, the dataset allows toutilize data-intense deep learning methods.

The POLUSA Dataset: 0.9M Political News Articles Balanced by Time and Outlet Popularity