https://journals.umt.edu.pk/index.php/UMT-AIR <p style="text-align: justify;"><strong>UMT Artificial Intelligence Review (UMT-AIR)</strong> is a double-blind peer-reviewed biannual journal that provides a wide variety of perspectives on the theory and practices of work in the realm of AI. We welcome research papers on foundational and applied work, as well as case studies. UMT-AIR also invites studies on critical analytical studies on AI applications, which present an in-depth evaluation of the AI tools and methods being employed.</p> en-US <p style="text-align: justify;"><em>UMT-AIR </em>follow an open-access publishing policy and full text of all published articles is available free, immediately upon publication of an issue. The journal’s contents are published and distributed under the terms of the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International</a> (<a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a>) license. Thus, the work submitted to the journal implies that it is original, unpublished work of the authors (neither published previously nor accepted/under consideration for publication elsewhere). On acceptance of a manuscript for publication, a corresponding author on the behalf of all co-authors of the manuscript will sign and submit a completed&nbsp;the Copyright and Author Consent Form.</p> [email protected] (UMT-AIR) [email protected] (Editorial Assistant) Tue, 21 Apr 2026 11:39:58 +0500 OJS 3.1.2.1 http://blogs.law.harvard.edu/tech/rss 60 https://journals.umt.edu.pk/index.php/UMT-AIR/article/view/7913 <p>Social media platforms have changed the communication process in such a radical way that now it takes just a few moments for unverified or fake news to reach millions of users. While in the past couple of years various methods have been developed to fight against the spread of false rumors, there are still many challenges to face. These challenges include dealing with noisy data, fast detection, and offering actionable insights for giving quick solutions. The current study aimed to put forward a conceptual noise-aware graph-based framework for the early detection of false rumors on social media. The framework melds together robustness, propagation-aware graph representations, and lightweight early-warning mechanisms in a single design. In order to depict the propagation patterns, the framework makes use of Graph Neural Networks (GNNs) for graph-based propagation modeling and also supports an uncertainty estimation module to handle data biases and incompleteness. One of the main features of the framework is the pinpointing of the most influential posts, users, and propagation paths that have a direct effect on the detection outcomes. While this work is not centered around experimental results, it lays out in detail the real-world validation plan using benchmark datasets, such as Twitter15/16, Weibo, and FakeNewsNet. The integration of noise-awareness, early detection, and interpretability in a single framework is the first step in the direction of robust and explainable rumor detection, thus opening new avenues for future theoretical and experimental work in this field.</p> ABU BAKAR SHABBIR, Usama Husnain Copyright (c) 2026 ABU BAKAR SHABBIR, Usama Husnain https://creativecommons.org/licenses/by/4.0 https://journals.umt.edu.pk/index.php/UMT-AIR/article/view/7913 Mon, 20 Apr 2026 00:00:00 +0500