Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/1642
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dc.contributor.authorRapti, Matina-
dc.contributor.authorTsakalidis, George-
dc.contributor.authorPetridou, Sophia-
dc.contributor.authorVergidis, Kostas-
dc.date.accessioned2023-11-01T11:32:57Z-
dc.date.available2023-11-01T11:32:57Z-
dc.date.issued2022-
dc.identifier10.3390/info13070306en_US
dc.identifier.issn2078-2489en_US
dc.identifier.urihttps://doi.org/10.3390/info13070306en_US
dc.identifier.urihttps://ruomo.lib.uom.gr/handle/7000/1642-
dc.description.abstractThe 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.en_US
dc.language.isoenen_US
dc.sourceInformationen_US
dc.subjectFRASCATI::Natural sciences::Computer and information sciencesen_US
dc.subjectFRASCATI::Social sciences::Lawen_US
dc.subject.otherfake news overviewen_US
dc.subject.otherdisinformation blueprinten_US
dc.subject.otherdetection strategiesen_US
dc.titleFake News Incidents through the Lens of the DCAM Disinformation Blueprinten_US
dc.typeArticleen_US
dc.contributor.departmentΤμήμα Εφαρμοσμένης Πληροφορικήςen_US
local.identifier.volume13en_US
local.identifier.issue7en_US
local.identifier.firstpage306en_US
Appears in Collections:Department of Applied Informatics

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