A computational framework for understanding risk factors in cybercrime

Abstract

In this paper, we present a prototype computational framework to study the risk factors that play a relevant role in cybercrime. Intending to address the limitations of the classical approaches used in social science and the black box approaches commonly used in computational science, we propose an iterative study process that moves the focus towards the use of causal and Bayesian methodologies to obtain a better complete picture of these complex processes and guide the design of new experiments. In this paper, to exemplify our proposal, we also analyze data from a survey of Spanish children about their cyberbullying-related experiences and various personal, environmental, and technological factors that could play a key role in online victimization. The results of our study bring together approaches from social and computer science, highlighting the need for multidisciplinary studies to understand better the complex human process behind cybercrime.