  The news ecosystem has become increasingly complex, encompassing a wide range
of sources with varying levels of trustworthiness, and with public commentary
giving different spins to the same stories. In this paper, we present a
multi-platform measurement of this ecosystem. We compile a list of 1,073 news
websites and extract posts from four Web communities (Twitter, Reddit, 4chan,
and Gab) that contain URLs from these sources. This yields a dataset of 38M
posts containing 15M news URLs, spanning almost three years.
  We study the data along several axes, assessing the trustworthiness of shared
news, designing a method to group news articles into stories, analyzing these
stories are discussed and measuring the influence various Web communities have
in that. Our analysis shows that different communities discuss different types
of news, with polarized communities like Gab and /r/The_Donald subreddit
disproportionately referencing untrustworthy sources. We also find that fringe
communities often have a disproportionate influence on other platforms w.r.t.
pushing narratives around certain news, for example about political elections,
immigration, or foreign policy.
