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Building an Explainable and Scalable AI System for Fake News Detection Across Digital Platforms
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 15 |
| Year of Publication: 2025 |
| Authors: Harsh Rathod, Durvesh Shelar, Rudrapratap Singh, Niki Modi |
10.5120/ijca2025925179
|
Harsh Rathod, Durvesh Shelar, Rudrapratap Singh, Niki Modi . Building an Explainable and Scalable AI System for Fake News Detection Across Digital Platforms. International Journal of Computer Applications. 187, 15 ( Jun 2025), 34-42. DOI=10.5120/ijca2025925179
Abstract
With the exponential rise of digital content and the ubiquity of social media, the spread of both accurate and deceptive information has become increasingly difficult to control. Fake news, often crafted to influence public perception, generate engagement, or propagate bias, presents a growing threat to societal trust and democratic integrity. This paper introduces a robust AI-powered system for detecting fake news, utilizing advanced machine learning and natural language processing (NLP) techniques. The proposed model analyzes textual cues, emotional tone, dissemination patterns, and audience response to distinguish false information from credible content at an early stage. Combining deep learning architectures with hybrid information propagation networks, the system enhances detection performance across varied content types. The study also underscores the importance of transparency, multi-language adaptability, and real-time analysis to effectively combat the evolving nature of misinformation. Future enhancements are discussed to improve interpretability and cross-platform deployment.
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