  In the recent political climate, the topic of news quality has drawn
attention both from the public and the academic communities. The growing
distrust of traditional news media makes it harder to find a common base of
accepted truth. In this work, we design and build MediaRank
(www.media-rank.com), a fully automated system to rank over 50,000 online news
sources around the world. MediaRank collects and analyzes one million news
webpages and two million related tweets everyday. We base our algorithmic
analysis on four properties journalists have established to be associated with
reporting quality: peer reputation, reporting bias / breadth, bottomline
financial pressure, and popularity.
  Our major contributions of this paper include: (i) Open, interpretable
quality rankings for over 50,000 of the world's major news sources. Our
rankings are validated against 35 published news rankings, including French,
German, Russian, and Spanish language sources. MediaRank scores correlate
positively with 34 of 35 of these expert rankings. (ii) New computational
methods for measuring influence and bottomline pressure. To the best of our
knowledge, we are the first to study the large-scale news reporting citation
graph in-depth. We also propose new ways to measure the aggressiveness of
advertisements and identify social bots, establishing a connection between both
types of bad behavior. (iii) Analyzing the effect of media source bias and
significance. We prove that news sources cite others despite different
political views in accord with quality measures. However, in four
English-speaking countries (US, UK, Canada, and Australia), the highest ranking
sources all disproportionately favor left-wing parties, even when the majority
of news sources exhibited conservative slants.
