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.
翻译:网络入侵已经在各种背景和在线社会平台中进行了研究,并且以不同数据为模型,使用最先进的机器和深层次的学习算法进行模型,以便能够自动检测和阻止这种行为。用户可能因为自身(在线)社会圈中的毒性和侵犯性而受到影响,从而受到攻击性或甚至欺凌他人的影响。实际上,这种行为可以从一个用户和邻里传播到另一个用户,从而在网络中传播。有趣的是,根据我们的知识,没有任何工作模拟了侵略行为的网络动态。在本文中,我们迈出了朝这个方向迈出的第一步,利用观点动态研究在社交媒体上传播侵略行为。我们提出了如何从一个用户向另一个用户传播攻击行为的模式,这取决于每个用户如何与其他侵略性或经常用户联系起来。我们通过对推特数据进行广泛的模拟,研究如何在网络中传播侵略性行为。我们用爬行和附加说明的地面真相数据验证我们的模型,达到80%的ACU,并讨论我们工作的结果和影响。