A Reference Ontology for Information Disorder

• Athus Cavalini UFES
• Giovanni Comarela UFES
• João Paulo A. Almeida UFES

DOI: https://doi.org/10.5753/webmedia.2026.30335

Resumo

The contemporary information ecosystem is being increasingly affected by information disorder, an overarching phenomenon encompassing misinformation, disinformation, and malinformation. In response to the growing need to systematically analyze and compare information disorder phenomena across platforms and contexts, the Information Disorder framework was proposed to conceptualize misinformation, disinformation, and malinformation, as well as the agents, messages, and phases involved in their production and circulation. Building on this framework, this paper proposes IDONT, a Reference Ontology for Information Disorder grounded in OntoUML and the Unified Foundational Ontology (UFO). IDONT models information disorder as a socio-temporal phenomenon and explicitly represents phase-dependent roles and motivations, enabling structured modeling of how agents participate in the creation, production, and distribution of this type of content. The ontology was operationalized in OWL and evaluated using automated reasoning and SPARQL queries over an instantiated case study based on the historical War of the Worlds radio broadcast. The results indicate that IDONT provides a semantically grounded conceptual foundation for the annotation, integration, and analytical investigation of information disorder phenomena.

Palavras-chave: Information Disorder, Reference Ontology, Disinformation, Misinformation, UFO, OntoUML

Referências

João Paulo A. Almeida, Giancarlo Guizzardi, Tiago Prince Sales, and Claudenir M. Fonseca. 2026. gUFO: A Gentle Foundational Ontology for Semantic Web Knowledge Graphs. arXiv:2603.20948 [cs.AI] [link]

Muhammad Amith and Cui Tao. 2018. Representing vaccine misinformation using ontologies. Journal of Biomedical Semantics 9, 22 (2018), 1–13.

Leandro Araujo, João Couto, Luiz Nery, Isadora Rodrigues, Jussara Almeida, Julio Reis, and Fabricio Benevenuto. 2024. Finding Fake News Websites in the Wild. In Proceedings of the 30th Brazilian Symposium on Multimedia and the Web (Juiz de Fora/MG). SBC, Porto Alegre, RS, Brasil, 171–178. DOI: 10.5753/webmedia.2024.243115

Anoud Bani-Hani, Oluwasegun Adedugbe, Elhadj Benkhelifa, Munir Majdalawieh, and Feras Al-Obeidat. 2020. A semantic model for context-based fake news detection on social media. In 2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA). IEEE, 1–7.

Athus Cavalini, Thamya Donadia, Fabio Malini, and Giovanni Comarela. 2024. Detecting Misinformation on Telegram Antivaccine Communities. In Anais do XXXIX Simpósio Brasileiro de Bancos de Dados (Florianópolis/SC). SBC, Porto Alegre, RS, Brasil, 729–735. DOI: 10.5753/sbbd.2024.243188

K Chandrasekaran, A Kandasamy, M Venkatesan, P Prabhavathi, M Gokuldhev, and C Aishwarya. 2023. Ontology for Contextual Fake News Assessment based on Text and Images. In Preprint / Working Paper.

Matheus Lenke Coutinho. 2023. Tonto: A Textual Language for Ontology-Driven Conceptual Modeling. Master’s thesis. Universidade Federal do Espírito Santo (UFES), Centro Tecnológico, Colegiado do Curso de Ciência da Computação, Vitória, ES.

Ricardo de Almeida Falbo. 2014. SABiO: Systematic Approach for Building Ontologies. In Proceedings of the Ontology and Conceptual Modeling Research Group Workshop (OntoComODISE 2014) (CEUR Workshop Proceedings, Vol. 1301). 1–14. [link]

João Guilherme Bastos dos Santos and Tatiana Maria Dourado. 2020. Dissemination of Fake News in Cross-Platform Perspective: The Hydra of 2018 Brazilian Presidential Election. In Proceedings of the #AoIR 2nd Flashpoint Symposium 2020. Galoá, Porto Alegre, Brazil (Online). [link]

Giancarlo Guizzardi. 2005. Ontological foundations for structural conceptual models. PhD Thesis - Research UT, graduation UT. University of Twente.

Giancarlo Guizzardi, Alessander Botti Benevides, Claudenir M. Fonseca, Daniele Porello, João Paulo A. Almeida, and Tiago Prince Sales. 2022. UFO: Unified Foundational Ontology. Applied Ontology 17, 1 (2022), 167–210. DOI: 10.3233/AO-210256

Chelsea Hicks. 2017. An Ontological Approach to Misinformation: Quickly Finding Relevant Information. In Hawaii International Conference on System Sciences (HICSS). URI: [link].

Svitlana Mazepa, Sergiy Pugach, Serhiy Banakh, Oleksandra Yavorska, Andriy Melnyk, and Natalia Golota. 2021. An Ontological Approach to Detecting Fake News in Online Media. In 2021 11th International Conference on Advanced Computer Information Technologies (ACIT). IEEE, 531–535.

Rishabh Misra and Jigyasa Grover. 2022. Do Not ‘Fake It Till You Make It’! Synopsis of Trending Fake News Detection Methodologies Using Deep Learning. 213–235. DOI: 10.1007/978-3-031-10869-3_12

Mark A. Musen. 2015. The protégé project: a look back and a look forward. AI Matters 1, 4 (2015), 4–12. DOI: 10.1145/2757001.2757003

João Olivetti, Ana Bomfim, Marcos Oliveira, Helom Marques, Juan Avelar, Julio Reis, and Philipe Melo. 2025. O Fenômeno da Desinformação no WhatsApp, Telegram e Outras Plataformas de Mensagens Instantâneas: Uma Revisão Sistemática da Literatura. In Proceedings of the 31st Brazilian Symposium on Multimedia and the Web (Rio de Janeiro/RJ). SBC, Porto Alegre, RS, Brasil, 661–675. DOI: 10.5753/webmedia.2025.16058

Marcelo Santos, Chang Sup Park, João Guilherme Bastos dos Santos, Athus Cavalini, and Homero Gil de Zúñiga. 2025. Latent bombs of disinformation. Information, Communication & Society 28, 16 (2025), 2868–2889. DOI: 10.1080/1369118X.2025.2492575

Brad Schwartz. 2018. Orson Welles and the Birth of Fake News. The New York Times (30 October 2018). [link] Accessed: 2026-02-26.

Kai Shu, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, and Huan Liu. 2018. FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media. arXiv preprint arXiv:1809.01286 (2018).

Visual Paradigm. 2026. Visual Paradigm. [link] Computer software.

Claire Wardle and Hossein Derakhshan. 2017. Information disorder: Toward an interdisciplinary framework for research and policy making. [link]

Lina Zhou and Dongsong Zhang. 2007. An Ontology-Supported Misinformation Model: Toward a Digital Misinformation Library. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans 37, 5 (2007), 804–813.

Hussaini Abubakar Zubairu and Enesi Femi Aminu. 2024. Towards an Improved Semantic Model: An Ontological Based Framework for Misinformation Detection. SSRN (2024).