Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Prabhavathy, P.Subject: search.filters.subject.Fake news

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    Ontology for Contextual Fake News Assessment Based on Text and Images
    (Institute of Electrical and Electronics Engineers Inc., 2024) Chandrasekaran, K.; Kandasamy, A.; Venkatesan, M.; Prabhavathy, P.; Gokuldhev, M.; Aishwarya, C.
    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. © 2024 IEEE.
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    Grapevine SDLC Model for Real-Time Fake News Classification
    (Springer Science and Business Media Deutschland GmbH, 2025) Aishwarya, C.; Shekokar, T.P.; Naga Mukesh, K.; Venkatesan, M.; Prabhavathy, P.
    In an era of rapid information distribution, the presence of fake news presents enormous difficulties to society, influencing public opinion and decision-making on a global scale. To address this issue, a reliable and efficient system capable of detecting and classifying fake news in real time must be developed. This project proposes the design and implementation of a specialized Software Development Life Cycle (SDLC) model, called the Grapevine SDLC, specifically designed for developing a real-time fake news classifier using Large Language Models (LLMs) and Apache Kafka. The Grapevine SDLC takes a methodical, iterative approach, starting with a thorough requirements analysis that identifies both system capabilities and limitations. During the design and development phase, the system architecture is crafted with a focus on scalability and real-time processing, integrating LLMs for highly accurate content analysis and categorization. Kafka’s distributed messaging platform ensures seamless and efficient data streaming, enabling the system to handle large volumes of data in real time. Further, the model includes continuous monitoring and feedback loops to improve detection accuracy and adapt to evolving fake news patterns. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.