  The present level of proliferation of fake, biased, and propagandistic
content online has made it impossible to fact-check every single suspicious
claim or article, either manually or automatically. Thus, many researchers are
shifting their attention to higher granularity, aiming to profile entire news
outlets, which makes it possible to detect likely "fake news" the moment it is
published, by simply checking the reliability of its source. Source factuality
is also an important element of systems for automatic fact-checking and "fake
news" detection, as they need to assess the reliability of the evidence they
retrieve online. Political bias detection, which in the Western political
landscape is about predicting left-center-right bias, is an equally important
topic, which has experienced a similar shift towards profiling entire news
outlets. Moreover, there is a clear connection between the two, as highly
biased media are less likely to be factual; yet, the two problems have been
addressed separately. In this survey, we review the state of the art on media
profiling for factuality and bias, arguing for the need to model them jointly.
We further discuss interesting recent advances in using different information
sources and modalities, which go beyond the text of the articles the target
news outlet has published. Finally, we discuss current challenges and outline
future research directions.
