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

Words are the Window to the Soul: Language-based User Representations for Fake News Detection