  Aggression in online social networks has been studied mostly from the
perspective of machine learning which detects such behavior in a static
context. However, the way aggression diffuses in the network has received
little attention as it embeds modeling challenges. In fact, modeling how
aggression propagates from one user to another, is an important research topic
since it can enable effective aggression monitoring, especially in media
platforms which up to now apply simplistic user blocking techniques. In this
paper, we address aggression propagation modeling and minimization on Twitter,
since it is a popular microblogging platform at which aggression had several
onsets. We propose various methods building on two well-known diffusion models,
Independent Cascade (IC) and Linear Threshold (LT), to study the aggression
evolution in the social network. We experimentally investigate how well each
method can model aggression propagation using real Twitter data, while varying
parameters, such as seed users selection, graph edge weighting, users'
activation timing, etc. It is found that the best performing strategies are the
ones to select seed users with a degree-based approach, weigh user edges based
on their social circles' overlaps, and activate users according to their
aggression levels. We further employ the best performing models to predict
which ordinary real users could become aggressive (and vice versa) in the
future, and achieve up to AUC=0.89 in this prediction task. Finally, we
investigate aggression minimization by launching competitive cascades to
"inform" and "heal" aggressors. We show that IC and LT models can be used in
aggression minimization, providing less intrusive alternatives to the blocking
techniques currently employed by popular online social network platforms.
