  The popularity of online social networks has enabled rapid dissemination of
information. People now can share and consume information much more rapidly
than ever before. However, low-quality and/or accidentally/deliberately fake
information can also spread rapidly. This can lead to considerable and negative
impacts on society. Identifying, labelling and debunking online misinformation
as early as possible has become an increasingly urgent problem. Many methods
have been proposed to detect fake news including many deep learning and
graph-based approaches. In recent years, graph-based methods have yielded
strong results, as they can closely model the social context and propagation
process of online news. In this paper, we present a systematic review of fake
news detection studies based on graph-based and deep learning-based techniques.
We classify existing graph-based methods into knowledge-driven methods,
propagation-based methods, and heterogeneous social context-based methods,
depending on how a graph structure is constructed to model news related
information flows. We further discuss the challenges and open problems in
graph-based fake news detection and identify future research directions.
