  Predicting the political bias and the factuality of reporting of entire news
outlets are critical elements of media profiling, which is an understudied but
an increasingly important research direction. The present level of
proliferation of fake, biased, and propagandistic content online, has made it
impossible to fact-check every single suspicious claim, either manually or
automatically. Alternatively, we can profile entire news outlets and look for
those that are likely to publish fake or biased content. This approach makes it
possible to detect likely "fake news" the moment they are published, by simply
checking the reliability of their source. From a practical perspective,
political bias and factuality of reporting have a linguistic aspect but also a
social context. Here, we study the impact of both, namely (i) what was written
(i.e., what was published by the target medium, and how it describes itself on
Twitter) vs. (ii) who read it (i.e., analyzing the readers of the target medium
on Facebook, Twitter, and YouTube). We further study (iii) what was written
about the target medium on Wikipedia. The evaluation results show that what was
written matters most, and that putting all information sources together yields
huge improvements over the current state-of-the-art.
