This deliverable represents the first outcome of Task 6.3 “Profile matching and definition of risk indicators for potential young victims and offenders”, which is the third task of Work Package (WP) 6 in the RAYUELA project. This task obtains as input (from previous tasks) a series of potentially key identifiers/variables for detecting or probabilistically classifying the participants of the pilots. This also helps to create a series of risk profiles (of offender and victim) for the cybercrimes under consideration. However, the approach used in this task is based on a different mindset than the one used in Task 6.2 (based on Machine Learning predictions). In this task, an approach based on causality and Bayesian statistics is used. 

More specifically, we have analysed the data collected in the RAYUELA pilots using Bayesian Networks, and proposed a methodology to compare different hypotheses of architectures quantitatively. We have also been assisted by RAYUELA cyberbullying experts for proposing such architectures. Subsequently, we perform a series of causal statistical analyses that help us to identify essential indicators/variables to identify potential victims and perpetrators. Finally, we note a series of comments and limitations on the techniques used and the data available so far, which make us cautious about the conclusions that can be drawn from the results. 

We focused the analyses on the cybercrime of cyberbullying, as it is the only cybercrime considered for which we have a validated psychological questionnaire that players must answer. This would be the gathered data which is closer to a “ground truth”. In this way, the methodologies and conclusions drawn from these analyses of cyberbullying will be helpful for the subsequent investigation of the other cybercrimes considered in RAYUELA. 

Based on the initial findings, it appears that the variables collected through the RAYUELA serious game are promising in detecting potential perpetrators and victims of cyberbullying. However, it is important to exercise caution in interpreting the results due to the limited amount of data available for analysis, as well as the potential noise inherent in social science and video game data. Nevertheless, these initial results suggest that the RAYUELA serious game has the potential to be a valuable tool for social research purposes, highlighting the need for further exploration of its capabilities.