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OntoFD: A Generic Social Media Fake News Ontology

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Model and Data Engineering (MEDI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14396))

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Abstract

Fake news has increased dramatically in recent years as a result of social media’s rapid and explosive expansion. To lessen their negative consequences, detecting them has become essential and a very busy and dynamic area of research. Despite the fact that much progress has been made in this field, it remains a difficult task due to its complexity. Indeed, there are various types of fake news. The news itself is made up of several data components of various types (textual, graphical, multimedia, network, social, psychological, and so on). It also involves multiple actors: a creator, a victim, and a target community. To handle all of this knowledge and improve the ability to recognize fake news on social media, this study proposes an ontological structure for its depiction. The result is an ontology with the name OntoFD. It shows a hierarchy of concepts and relations that can be used to assess whether information propagating on social media is credible or not.

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Author information

Authors and Affiliations

  1. RIADI, National School of Computer Sciences, Manouba University, Manouba, Tunisia

    Fériel Ben Fraj

  2. SMART, Higher Institute of Management, University of Tunis, Tunis, Tunisia

    Nourhène Nouri

Authors
  1. Fériel Ben Fraj
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  2. Nourhène Nouri
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Correspondence to Fériel Ben Fraj .

Editor information

Editors and Affiliations

  1. Bordeaux INP, Talence, France

    Mohamed Mosbah

  2. Dublin City University, Dublin, Ireland

    Tahar Kechadi

  3. ENSMA, Poitiers, France

    Ladjel Bellatreche

  4. University of Sfax, Sfax, Tunisia

    Faiez Gargouri

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Ben Fraj, F., Nouri, N. (2024). OntoFD: A Generic Social Media Fake News Ontology. In: Mosbah, M., Kechadi, T., Bellatreche, L., Gargouri, F. (eds) Model and Data Engineering. MEDI 2023. Lecture Notes in Computer Science, vol 14396. Springer, Cham. https://doi.org/10.1007/978-3-031-49333-1_13

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  • DOI: https://doi.org/10.1007/978-3-031-49333-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49332-4

  • Online ISBN: 978-3-031-49333-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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