Enhancing social science research on cyberbullying through human machine collaboration.

ABSTRACT: Cyberbullying (CB) has emerged as a growing concern among adolescents, with nearly 10% of European children affected monthly and almost half experiencing it at least once. Unlike traditional bullying, CB thrives in digital environments where anonymity and impunity are prevalent. Despite its increasing prevalence, understanding the causal mechanisms behind CB remains challenging due to the limitations of conventional statistical methods, which often rely on correlations and are prone to spurious associations. In this paper, we introduce a novel human-machine consensus framework for causal discovery, aimed at supporting social scientists in unraveling the complex dynamics of CB. We leverage recent advances in data-driven causal inference, particularly the use of Directed Acyclic Graphs (DAGs

SUBMITTER: Banos-Ramos A 

PROVIDER: S-EPMC12475109 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature