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Characterizing Fake News: A Conceptual Modeling-based Approach

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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https://hal.archives-ouvertes.fr/hal-03697974
Contributor : Nicolas Belloir Connect in order to contact the contributor
Submitted on : Friday, June 17, 2022 - 2:32:33 PM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM

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  • HAL Id : hal-03697974, version 1

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Nicolas Belloir, Wassila Ouerdane, Oscar Pastor. Characterizing Fake News: A Conceptual Modeling-based Approach. ER 2022 - 41st International Conference on Conceptual Modeling, Oct 2022, Hyderabad, India. ⟨hal-03697974⟩

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