FC-Kit: an intelligent fact-checking system for preventing fake news spread in social media through active plugins
subject: CanineNet News Sentinel; deep learning; fact-checking; fake news detection; misinformation; Natural Language Processing (NLP); social media
description: The rapid expansion of social media has intensified the spread of misinformation, threatening public trust, informed decision-making and societal stability. This paper introduces the Fact-Checking Kit (FC-Kit), a plugin-based, real-time misinformation detection framework designed for seamless integration into social media platforms. At its core, the system employs the proposed CanineNet News Sentinel (CnNS) model, which incorporates advanced algorithms for detecting fake news while also assessing bias indicators, identifying clickbait headlines, detecting poor text framing and calculating an article credibility rate. Experimental evaluations on benchmark datasets Twitter and Twitch demonstrate that FC-Kit achieves 99% detection accuracy and reduces computational time by 41.4% compared to state-of-the-art methods. Unlike conventional fact-checking systems, FC-Kit actively tracks the news dissemination chain, enabling early intervention before misinformation gains traction. Its modular plugin architecture supports real-time analysis, ensuring media literacy promotion and fostering critical thinking among users. By combining content credibility scoring with advanced detection features, FC-Kit offers a scalable and practical solution for social media platforms, fact-checking organizations and researchers committed to combating online misinformation. This work advances the state-of-the-art in misinformation detection and emphasizes the necessity of embedding automated fact-checking tools directly into social media platforms. 2025 Informa UK Limited, trading as Taylor & Francis Group.
creator: Sethurajan, Monikka Reshmi; K, Natarajan
source: Journal of Cyber Security Technology;
publisher: Taylor and Francis Ltd.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISSN: 23742917;
format: online
language: English
type: Article
coverage: Sethurajan M.R., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Bengaluru, India; K N., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Bengaluru, India