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Category: Data

Description: The useNews dataset has been compiled to enable the study of online news engagement. It relies on the MediaCloud and CrowdTangle APIs as well as on data from the Reuters Digital News Report. The entire dataset builds on data from 2019 and 2020 as well as a total of 12 countries. It is free to use (subject to citing/referencing it). The data originates from both the 2019 and the 2020 Reuters Digital News Report (http://www.digitalnewsreport.org/), media content from MediaCloud (https://mediacloud.org/) for 2019 and 2020 from all news outlets that have been used most frequently in the respective year according to the survey data, and engagement metrics for all available news-article URLs through CrowdTangle (https://www.crowdtangle.com/). To start using the data, a total of eight data objects exist, namely one each for 2019 and 2020 for the survey, news-article meta information, news-article DFM's, and engagement metrics. To make your life easy, we've provided several packaged download options: - survey data for 2019, 2020, or both (also available in CSV format) - news-article meta data for 2019, 2020, or both (also available in CSV format) - news-article DFM's for 2019, 2020, or both - engagement data for 2019, 2020, or both (also available in CSV format) - all of 2019 or 2020 Also, if you are working with R, we have prepared a simple file to automatically download all necessary data (~1.5 GByte) at once: https://osf.io/fxmgq/ Note that all .rds files are .xz-compressed, which shouldn't bother you when you are in R. You can import all the .rds files through `variable_name <- readRDS('filename.rds')`, .RData (also .xz-compressed) can be imported by simply using `load('filename.RData')` which will load several already named objects into your R environment. To import data through other programming languages, we also provide all data in respective CSV files. These files are rather large, however, which is why we have also .xz-compressed them. DFM's, unfortunately, are not available as CSV's due to their sparsity and size. Find out more about the data variables and dig into plenty of examples in the useNews-examples workbook: https://osf.io/snuk2/