Grapevine SDLC Model for Real-Time Fake News Classification

Abstract

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.