Tundis, Andrea ; Shams, Ahmed Ali ; Mühlhäuser, Max (2023)
From the detection towards a pyramidal classification of terrorist propaganda.
In: Journal of Information Security and Applications, 79
doi: 10.1016/j.jisa.2023.103646
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
With over 3,81 billion users from across the world, social media platforms provide a borderless environment for people of different nationalities, races, ethnicities, and religious beliefs to interact and communicate with each other. Not only for legitimate purposes are these digital tools used, but also groups of extremists and terrorist organizations take advantage of the features of these platforms to spread radicalization, propaganda, brainwashing and for online recruitment. Recent studies, conducted in this area, are trying to face with this phenomenon, but due to the heterogeneity of the sources, the large amount of daily data generated, and especially the different levels of radicalization of the users, make it even more difficult to define effective and general countermeasures against such phenomenon. In this context, the paper provides a solution that not only aims to support the detection of terrorist propaganda (and related users), but also to support its further categorization, centered on a pyramid classification model, by analyzing the level of users’ radicalization. This approach has two fundamental complementary advantages, as on the one hand it enables the establishment of priorities in terms of intervention, and on the other hand to define and apply targeted countermeasures based on the level of user’s radicalization. The proposed model and the results obtained from its experimentation are shown and discussed in comparison to previous works.
Typ des Eintrags: | Artikel |
---|---|
Erschienen: | 2023 |
Autor(en): | Tundis, Andrea ; Shams, Ahmed Ali ; Mühlhäuser, Max |
Art des Eintrags: | Bibliographie |
Titel: | From the detection towards a pyramidal classification of terrorist propaganda |
Sprache: | Englisch |
Publikationsjahr: | 1 Dezember 2023 |
Verlag: | Elsevier |
Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Journal of Information Security and Applications |
Jahrgang/Volume einer Zeitschrift: | 79 |
DOI: | 10.1016/j.jisa.2023.103646 |
URL / URN: | https://www.sciencedirect.com/science/article/abs/pii/S22142... |
Kurzbeschreibung (Abstract): | With over 3,81 billion users from across the world, social media platforms provide a borderless environment for people of different nationalities, races, ethnicities, and religious beliefs to interact and communicate with each other. Not only for legitimate purposes are these digital tools used, but also groups of extremists and terrorist organizations take advantage of the features of these platforms to spread radicalization, propaganda, brainwashing and for online recruitment. Recent studies, conducted in this area, are trying to face with this phenomenon, but due to the heterogeneity of the sources, the large amount of daily data generated, and especially the different levels of radicalization of the users, make it even more difficult to define effective and general countermeasures against such phenomenon. In this context, the paper provides a solution that not only aims to support the detection of terrorist propaganda (and related users), but also to support its further categorization, centered on a pyramid classification model, by analyzing the level of users’ radicalization. This approach has two fundamental complementary advantages, as on the one hand it enables the establishment of priorities in terms of intervention, and on the other hand to define and apply targeted countermeasures based on the level of user’s radicalization. The proposed model and the results obtained from its experimentation are shown and discussed in comparison to previous works. |
Zusätzliche Informationen: | Art.No.: 103646 |
Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Telekooperation LOEWE LOEWE > LOEWE-Zentren LOEWE > LOEWE-Zentren > emergenCITY |
Hinterlegungsdatum: | 30 Nov 2023 13:36 |
Letzte Änderung: | 05 Feb 2024 11:22 |
PPN: | 515247693 |
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