Identifying Possible Rumor Spreaders on Twitter: A Weak Supervised
  Learning Approach
  Online Social Media (OSM) platforms such as Twitter, Facebook are extensively
exploited by the users of these platforms for spreading the (mis)information to
a large audience effortlessly at a rapid pace. It has been observed that the
misinformation can cause panic, fear, and financial loss to society. Thus, it
is important to detect and control the misinformation in such platforms before
it spreads to the masses. In this work, we focus on rumors, which is one type
of misinformation (other types are fake news, hoaxes, etc). One way to control
the spread of the rumors is by identifying users who are possibly the rumor
spreaders, that is, users who are often involved in spreading the rumors. Due
to the lack of availability of rumor spreaders labeled dataset (which is an
expensive task), we use publicly available PHEME dataset, which contains rumor
and non-rumor tweets information, and then apply a weak supervised learning
approach to transform the PHEME dataset into rumor spreaders dataset. We
utilize three types of features, that is, user, text, and ego-network features,
before applying various supervised learning approaches. In particular, to
exploit the inherent network property in this dataset (user-user reply graph),
we explore Graph Convolutional Network (GCN), a type of Graph Neural Network
(GNN) technique. We compare GCN results with the other approaches: SVM, RF, and
LSTM. Extensive experiments performed on the rumor spreaders dataset, where we
achieve up to 0.864 value for F1-Score and 0.720 value for AUC-ROC, shows the
effectiveness of our methodology for identifying possible rumor spreaders using
the GCN technique.
