Abstract
For some time, and even more so now, Fake News has increasingly occupied the media and social space. How identify Fake News and conspiracy theories have become an extremely attractive research area. However, the lack of a solid and well-founded conceptual characterization of what exactly Fake News is and what are its main characteristics, makes it difficult to manage their understanding, identification, and detection. This research work advocates that conceptual modeling must plays a crucial role in characterizing Fake News content accurately. Only by delimiting what Fake News is will it be possible to understand and manage their different perspectives and dimensions, with the ultimate goal of developing a reliable framework for online Fake News detection, as much automated as possible. To contribute in that direction from a pure and practical conceptual modeling perspective, this paper proposes a precise conceptual model of Fake Newss, an essential element for any explainable Artificial Intelligence (XAI)-based approach that must be based on the shared understanding of the domain that only such an accurate conceptualization dimension can facilitate.
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Acknowledgment
The authors would like to thank the final year engineering students of the Military Academy of St-Cyr Coëtquidan who worked on this project: Glenn Le Roux, Gaspard Croizat, Hugo Fouché, Émilien Frugier and Louis-Antoine Nicolazo De Barmon.
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Belloir, N., Ouerdane, W., Pastor, O. (2022). Characterizing Fake News: A Conceptual Modeling-based Approach. In: Ralyté, J., Chakravarthy, S., Mohania, M., Jeusfeld, M.A., Karlapalem, K. (eds) Conceptual Modeling. ER 2022. Lecture Notes in Computer Science, vol 13607. Springer, Cham. https://doi.org/10.1007/978-3-031-17995-2_9
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