Ontology for Contextual Fake News Assessment Based on Text and Images

The 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...

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Bibliographic Details
Main Authors: K, Chandrasekaran, A, Kandasamy, M, Venkatesan, P, Prabhavathi, M, Gokuldhev, C, Aishwarya
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Audience Behavior
Contextual
Data Analysis
Fake news
Ontologies
OWL
OWL Ontology
Psychology
Quantum Deep Learning
Quantum Machine Learning
Social Engineering
Social networking (online)
Source Credibility
Taxonomy
Visualization
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Summary:The 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.
ISSN:2377-5750
DOI:10.1109/PDP62718.2024.00034