  Cyberaggression has been studied in various contexts and online social
platforms, and modeled on different data using state-of-the-art machine and
deep learning algorithms to enable automatic detection and blocking of this
behavior. Users can be influenced to act aggressively or even bully others
because of elevated toxicity and aggression in their own (online) social
circle. In effect, this behavior can propagate from one user and neighborhood
to another, and therefore, spread in the network. Interestingly, to our
knowledge, no work has modeled the network dynamics of aggressive behavior. In
this paper, we take a first step towards this direction by studying propagation
of aggression on social media using opinion dynamics. We propose ways to model
how aggression may propagate from one user to another, depending on how each
user is connected to other aggressive or regular users. Through extensive
simulations on Twitter data, we study how aggressive behavior could propagate
in the network. We validate our models with crawled and annotated ground truth
data, reaching up to 80% AUC, and discuss the results and implications of our
work.
