  With the ever-increasing spread of misinformation on online social networks,
it has become very important to identify the spreaders of misinformation
(unintentional), disinformation (intentional), and misinformation refutation.
It can help in educating the first, stopping the second, and soliciting the
help of the third category, respectively, in the overall effort to counter
misinformation spread. Existing research to identify spreaders is limited to
binary classification (true vs false information spreaders). However, people's
intention (whether naive or malicious) behind sharing misinformation can only
be understood after observing their behavior after exposure to both the
misinformation and its refutation which the existing literature lacks to
consider. In this paper, we propose a labeling mechanism to label people as one
of the five defined categories based on the behavioral actions they exhibit
when exposed to misinformation and its refutation. However, everyone does not
show behavioral actions but is part of a network. Therefore, we use their
network features, extracted through deep learning-based graph embedding models,
to train a machine learning model for the prediction of the classes. We name
our approach behavioral forensics since it is an evidence-based investigation
of suspicious behavior which is spreading misinformation and disinformation in
our case. After evaluating our proposed model on a real-world Twitter dataset,
we achieved 77.45% precision and 75.80% recall in detecting the malicious
actors, who shared the misinformation even after receiving its refutation. Such
behavior shows intention, and hence these actors can rightfully be called
agents of disinformation spread.
