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

TL;DR

This paper introduces a language-based user representation model for fake news detection, demonstrating its effectiveness and analyzing the traits of fake news spreaders across datasets and social network structures.

Contribution

The paper presents a novel language-based user representation approach for fake news detection and provides an extended analysis of fake news spreaders' language traits and social graph relations.

Findings

Language-based user representations improve fake news detection.

Fake news spreaders share domain-independent linguistic traits.

Echo Chamber effect is observable through language and social connections.

Abstract

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.