Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensembles

TL;DR

This paper investigates the use of knowledge graph-based document representations for fake news detection, demonstrating that combining them with contextual models achieves state-of-the-art results in a large-scale evaluation.

Contribution

It introduces novel knowledge graph-based document representation methods and systematically evaluates their effectiveness in fake news classification, achieving competitive and state-of-the-art performance.

Findings

Knowledge graph representations are competitive with traditional methods.

Combining knowledge graph and contextual representations yields state-of-the-art results.

First large-scale evaluation of knowledge graph use in fake news detection.

Abstract

Increasing amounts of freely available data both in textual and relational form offers exploration of richer document representations, potentially improving the model performance and robustness. An emerging problem in the modern era is fake news detection -- many easily available pieces of information are not necessarily factually correct, and can lead to wrong conclusions or are used for manipulation. In this work we explore how different document representations, ranging from simple symbolic bag-of-words, to contextual, neural language model-based ones can be used for efficient fake news identification. One of the key contributions is a set of novel document representation learning methods based solely on knowledge graphs, i.e. extensive collections of (grounded) subject-predicate-object triplets. We demonstrate that knowledge graph-based representations already achieve competitive performance to conventionally accepted representation learners. Furthermore, when combined with existing, contextual representations, knowledge graph-based document representations can achieve state-of-the-art performance. To our knowledge this is the first larger-scale evaluation of how knowledge graph-based representations can be systematically incorporated into the process of fake news classification.