Cyberaggression has been found 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 . We look into various opinion dynamicsmodels widely used to model how opinions propagate through a network . We propose ways to enhance these classical models to accommodate 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 the models with ground truth crawled and annotated  data, reaching up to 80\% AUC . We discuss the results and implications of our work.
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.
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