Research Article
Adaptive machine learning and deep learning framework for real-time fake news detection
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 187 - Issue 55 |
| Published: November 2025 |
| Authors: Vidhi Chaudhari, Ravindra Patel, Dharamsinh Solanki, Rahul Patel, Arush Aaron John |
10.5120/ijca2025925962
|
Vidhi Chaudhari, Ravindra Patel, Dharamsinh Solanki, Rahul Patel, Arush Aaron John . Adaptive machine learning and deep learning framework for real-time fake news detection. International Journal of Computer Applications. 187, 55 (November 2025), 36-40. DOI=10.5120/ijca2025925962
@article{ 10.5120/ijca2025925962,
author = { Vidhi Chaudhari,Ravindra Patel,Dharamsinh Solanki,Rahul Patel,Arush Aaron John },
title = { Adaptive machine learning and deep learning framework for real-time fake news detection },
journal = { International Journal of Computer Applications },
year = { 2025 },
volume = { 187 },
number = { 55 },
pages = { 36-40 },
doi = { 10.5120/ijca2025925962 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2025
%A Vidhi Chaudhari
%A Ravindra Patel
%A Dharamsinh Solanki
%A Rahul Patel
%A Arush Aaron John
%T Adaptive machine learning and deep learning framework for real-time fake news detection%T
%J International Journal of Computer Applications
%V 187
%N 55
%P 36-40
%R 10.5120/ijca2025925962
%I Foundation of Computer Science (FCS), NY, USA
Abstract
The rapid dissemination of fake news on digital platforms poses a critical threat to public discourse, political stability, and public health. Traditional detection methods struggle to keep pace with the evolving tactics of misinformation campaigns, prompting the need for more intelligent and adaptive systems. This research explores the application of deep learning (DL) and machine learning (ML) techniques for early and accurate detection of fake news across various domains. Through a structured review of state-of-the-art approaches—including rule-based systems, classical ML classifiers, deep learning models, and transformer-based architectures—we highlight methodological advances, dataset limitations, and system-level integration challenges. The paper also proposes a deployment-ready architecture combining real-time detection, user feedback, and robust evaluation to bridge the gap between academic research and real-world application.
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