Meta-Path-based Fake News Detection Leveraging Multi-level Social Context Information

Seungwon Shin, Jian Cui, Kwanwoo Kim, Seung Ho Na

Abstract:

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. Therefore, more information is
required to improve fake news detection, such as the multi-level
social context (news publishers and engaged users in social media) information and the temporal information of user engagement.
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 of
user engagement is hard to be used along with multi-level social
context information, and 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 MetaPath, a composite relation connecting two node types, to extract
meaningful multi-level social context information without loss. We
then propose Meta-Path instance encoding and aggregation methods to capture the temporal information of user engagement and
learn news representation end-to-end. According to our experiment, Hetero-SCAN yields significant performance improvement
over state-of-the-art fake news detection methods.