  Social networks (SNs) are increasingly important sources of news for many
people. The online connections made by users allows information to spread more
easily than traditional news media (e.g., newspaper, television). However, they
also make the spread of fake news easier than in traditional media, especially
through the users' social network connections. In this paper, we focus on
investigating if the SNs' users connection structure can aid fake news
detection on Twitter. In particular, we propose to embed users based on their
follower or friendship networks on the Twitter platform, so as to identify the
groups that users form. Indeed, by applying unsupervised graph embedding
methods on the graphs from the Twitter users' social network connections, we
observe that users engaged with fake news are more tightly clustered together
than users only engaged in factual news. Thus, we hypothesise that the embedded
user's network can help detect fake news effectively. Through extensive
experiments using a publicly available Twitter dataset, our results show that
applying graph embedding methods on SNs, using the user connections as network
information, can indeed classify fake news more effectively than most
language-based approaches. Specifically, we observe a significant improvement
over using only the textual information (i.e., TF.IDF or a BERT language
model), as well as over models that deploy both advanced textual features
(i.e., stance detection) and complex network features (e.g., users network,
publishers cross citations). We conclude that the Twitter users' friendship and
followers network information can significantly outperform language-based
approaches, as well as the existing state-of-the-art fake news detection models
that use a more sophisticated network structure, in classifying fake news on
Twitter.
