Exploring the effective nodes of interventions for cyberbullying victimization and anxiety symptoms: A simulation-based network analysis

This research explores the connection between adolescent cyberbullying victimization and anxiety based purely on the application of two techniques, namely, the NodeIdentifyR algorithm and the Ising model. The analysis carried out with the Ising model reveals the central symptom to be Upset Jokes (CBV4) in the network. The NodeIdentifyR algorithm, therefore, produces two divergent pathways for treatment and prevention intervention. The focus of treatment should, however, primarily be in the reduction of anxiety symptoms, particularly Trouble Relaxing (ANX4) and Afraid (ANX7). For prevention-oriented interventions, some issues might be more effectively addressed in targeting CBV, especially Tease Fun (CBV2) and Upset Jokes (CBV4). This research shows that educational data mining enables the effective implementation of educational strategies. Thus, such applications enhance intervention effectiveness. This highlights the critical role played by IT-the improvement of quality and educational services by the promotion of students' well-being interventions via data.