Words are the Window to the Soul: Language-based User Representations for Fake News Detection
This work addresses fake news detection for social media platforms, but it is incremental as it builds on known links between language and traits.
The authors tackled fake news detection by creating language-based user representations from social media posts, showing these representations improve detection performance. They also found that language features of fake news spreaders are domain-independent and consistent across datasets, and observed evidence of the Echo Chamber effect.
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.