  The complexity and diversity of today's media landscape provides many
challenges for researchers studying news producers. These producers use many
different strategies to get their message believed by readers through the
writing styles they employ, by repetition across different media sources with
or without attribution, as well as other mechanisms that are yet to be studied
deeply. To better facilitate systematic studies in this area, we present a
large political news data set, containing over 136K news articles, from 92 news
sources, collected over 7 months of 2017. These news sources are carefully
chosen to include well-established and mainstream sources, maliciously fake
sources, satire sources, and hyper-partisan political blogs. In addition to
each article we compute 130 content-based and social media engagement features
drawn from a wide range of literature on political bias, persuasion, and
misinformation. With the release of the data set, we also provide the source
code for feature computation. In this paper, we discuss the first release of
the data set and demonstrate 4 use cases of the data and features: news
characterization, engagement characterization, news attribution and content
copying, and discovering news narratives.
