  Fake news, false or misleading information presented as news, has a
significant impact on many aspects of society, such as in politics or
healthcare domains. Due to the deceiving nature of fake news, applying Natural
Language Processing (NLP) techniques to the news content alone is insufficient.
The multi-level social context information (news publishers and engaged users
in social media) and temporal information of user engagement are important
information in fake news detection. The proper usage of this information,
however, introduces three chronic difficulties: 1) multi-level social context
information is hard to be used without information loss, 2) temporal
information is hard to be used along with multi-level social context
information, 3) news representation with multi-level social context and
temporal information is hard to be learned in an end-to-end manner. To overcome
all three difficulties, we propose a novel fake news detection framework,
Hetero-SCAN. We use Meta-Path to extract meaningful multi-level social context
information without loss. Meta-Path, a composite relation connecting two node
types, is proposed to capture the semantics in the heterogeneous graph. We then
propose Meta-Path instance encoding and aggregation methods to capture the
temporal information of user engagement and produce news representation
end-to-end. According to our experiment, Hetero-SCAN yields significant
performance improvement over state-of-the-art fake news detection methods.
