Fake News Incidents through the Lens of the DCAM Disinformation Blueprint

Abstract

The emergence of the Internet and web technologies has magnified the occurrence of disinformation events and the dissemination of online fake news items. Fake news is a phenomenon where fake news stories are created and propagated online. Such events occur with ever increasing frequency, they reach a wide audience, and they can have serious real-life consequences. As a result, disinformation events are raising critical public interest concerns as in many cases online news stories of fake and disturbing events have been perceived as being truthful. However, even at a conceptual level, there is not a comprehensive approach to what constitutes fake news with regard to the further classification of individual occurrences and the detection/mitigation of actions. This work identifies the emergent properties and entities involved in fake news incidents and constructs a disinformation blueprint (DCAM-DB) based on cybercrime incident architecture. To construct the DCAM-DB in an articulate manner, the authors present an overview of the properties and entities involved in fake news and disinformation events based on the relevant literature and identify the most prevalent challenges. This work aspires to enable system implementations towards the detection, classification, assessment, and mitigation of disinformation events and to provide a foundation for further quantitative and longitudinal research on detection strategies.