  Cognitive and social traits of individuals are reflected in language use.
Moreover, individuals who are prone to spread fake news online often share
common traits. Building on these ideas, we introduce a model that creates
representations of individuals on social media based only on the language they
produce, and use them to detect fake news. We show that language-based user
representations are beneficial for this task. We also present an extended
analysis of the language of fake news spreaders, showing that its main features
are mostly domain independent and consistent across two English datasets.
Finally, we exploit the relation between language use and connections in the
social graph to assess the presence of the Echo Chamber effect in our data.
