  With social media being a major force in information consumption, accelerated
propagation of fake news has presented new challenges for platforms to
distinguish between legitimate and fake news. Effective fake news detection is
a non-trivial task due to the diverse nature of news domains and expensive
annotation costs. In this work, we address the limitations of existing
automated fake news detection models by incorporating auxiliary information
(e.g., user comments and user-news interactions) into a novel reinforcement
learning-based model called \textbf{RE}inforced \textbf{A}daptive
\textbf{L}earning \textbf{F}ake \textbf{N}ews \textbf{D}etection (REAL-FND).
REAL-FND exploits cross-domain and within-domain knowledge that makes it robust
in a target domain, despite being trained in a different source domain.
Extensive experiments on real-world datasets illustrate the effectiveness of
the proposed model, especially when limited labeled data is available in the
target domain.
