An Interdisciplinary Assessment of the Prophylactic Educational Treatments to Misinformation and Disinformation

Abstract

Misinformation and Disinformation, both types of Information Disorder in the cyber world, operate on a systemic level. Several factors enabling their persistence, including laws, policies, and technological mediators, have been investigated in the literature. Cybersecurity frameworks and guidelines specify that the target victims, as part of the human factors, hold a degree of responsibility for the persistence of any threat. This dissertation re-shifts the lens to the intended targets of the falsehood attacks, the information consumers. This factor of consideration was investigated through its three-part, multi-method research phases. The first phase, an interdisciplinary qualitative exploration of the different styles and techniques of preventive/prophylactic educational campaigns and theories that can be effectively used in raising awareness of the information consumers by employing a focus group of experts, revealed that ALA’s CRAAP (Currency, Relevance, Authority, Accuracy, and Purpose) has the advantage and sustained its classical value over its counterpart prebunking (from the theory of inoculation). On the other hand, the second phase, where an outright comparison of the existing and suggested preventive treatments from the previous phase through quantifications of theories and actual user experimentations, revealed that users under the CRAAP treatment displayed greater detection accuracy (DV1) than the users on prebunking treatment. However, users on prebunking treatment resulted in a faster assessment time (DV2) compared to the users under the CRAAP group. Finally, in the third and final phase, this dissertation redesigned and improved the prevailing mis/disinformation predictive models by including the educational conditioning of users, as a factor, in forecasting, in multivariate format, their cyber risk from these attacks of deception. Beyond policy implications, this project’s significance includes its contribution to the improvement of methods in the scientific inquiry on the domain of risk modeling by combining self-reported data with digital trace data. Furthermore, this dissertation takes pride in its interdisciplinary design, which considers the best practices of the granular fields involved, allowing for a more thorough investigation and probe through integration.