  The spread of unwanted or malicious content through social media has become a
major challenge. Traditional examples of this include social network spam, but
an important new concern is the propagation of fake news through social media.
A common approach for mitigating this problem is by using standard statistical
classification to distinguish malicious (e.g., fake news) instances from benign
(e.g., actual news stories). However, such an approach ignores the fact that
malicious instances propagate through the network, which is consequential both
in quantifying consequences (e.g., fake news diffusing through the network),
and capturing detection redundancy (bad content can be detected at different
nodes). An additional concern is evasion attacks, whereby the generators of
malicious instances modify the nature of these to escape detection. We model
this problem as a Stackelberg game between the defender who is choosing
parameters of the detection model, and an attacker, who is choosing both the
node at which to initiate malicious spread, and the nature of malicious
entities. We develop a novel bi-level programming approach for this problem, as
well as a novel solution approach based on implicit function gradients, and
experimentally demonstrate the advantage of our approach over alternatives
which ignore network structure.
