Ontology for Contextual Fake News Assessment Based on Text and Images

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dc.contributor.authorChandrasekaran, K.
dc.contributor.authorKandasamy, A.
dc.contributor.authorVenkatesan, M.
dc.contributor.authorPrabhavathy, P.
dc.contributor.authorGokuldhev, M.
dc.contributor.authorAishwarya, C.
dc.date.accessioned2026-02-06T06:34:09Z
dc.date.issued2024
dc.description.abstractThe spread of false news on social networks is a major challenge in the digital age across various sectors, encompassing technology, politics, public health, and finance. This paper introduces an ontology-based method that combines text and image analysis to evaluate the accuracy of news stories in the context of social media. We investigate the role of social engineering tactics in crafting and dispersing fake news and advocate for a comprehensive multi-contextual perspective that covers content, source, social media, psychological, and impact aspects. Using OWL (Web Ontology Language), we present an ontology framework for assessing fake news, providing a structured approach to analyze text, visuals, audio, audience behavior, source credibility, and news propagation patterns. This framework serves as a foundation for advanced detection systems, contributing to the fight against digital misinformation. © 2024 IEEE.
dc.identifier.citationProceedings - 2024 32nd Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2024, 2024, Vol., , p. 191-198
dc.identifier.urihttps://doi.org/10.1109/PDP62718.2024.00034
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/29088
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectAudience Behavior
dc.subjectContextual
dc.subjectData Analysis
dc.subjectFake news
dc.subjectOWL Ontology
dc.subjectQuantum Deep Learning
dc.subjectQuantum Machine Learning
dc.subjectSocial Engineering
dc.subjectSource Credibility
dc.titleOntology for Contextual Fake News Assessment Based on Text and Images

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