The news ecosystem encompasses 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 measurement pipeline able to identify news articles that discuss the same story and trace how they are shared on multiple online communities. 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 15.6M unique news URLs, spanning almost three years. We study the data along several axes, assessing the trustworthiness of shared news stories, analyzing how they 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 t hat f ringe c ommunities o ften h ave a disproportionate influence o n o ther p latforms w.r.t. p ushing n arratives around certain news, for example, about political elections, immigration, or foreign policy. In fact, fringe communities are seemingly successful in influencing the discussion on false narratives about news events on mainstream social networks.