Fake News Detection Using Machine Learning

S. Sadia Fatima, S. Khaja Sameer, M. Mukesh Kumar, S. Inthiyaz, S. Khaja Chand, K. Pavan

2025

Abstract

The unchecked proliferation of fabricated narratives and manipulative content poses a critical threat to informed public discourse and societal decision- making. As digital ecosystems amplify misleading claims, deploying agile detection systems becomes imperative to counteract their influence. This study proposes a novel machine learning architecture designed to identify disinformation with enhanced accuracy and contextual adaptability over conventional techniques. By synthesizing linguistic sentiment evaluation, behavioral network dynamics, and source authenticity metrics, the framework evaluates content trustworthiness dynamically. Unlike static models reliant on pre-labelled datasets, our solution employs semi-supervised learning paired with a self-optimizing feedback loop, enabling iterative refinement as new data streams emerge. Furthermore, the system integrates auxiliary indicators such as anomalous user interaction trends and temporal propagation rates, allowing early identification of suspect content before it achieves virility. This adaptive methodology not only detects false narratives but also anticipates emerging manipulation tactics, fostering a more resilient information landscape.

Paper Citation

in Harvard Style

Fatima S., Sameer S., Kumar M., Inthiyaz S., Chand S. and Pavan K. (2025). Fake News Detection Using Machine Learning. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 588-592. DOI: 10.5220/0013886800004919

in Bibtex Style

@conference{icrdicct`2525,
author={S. Fatima and S. Sameer and M. Kumar and S. Inthiyaz and S. Chand and K. Pavan},
title={Fake News Detection Using Machine Learning},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={588-592},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013886800004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Fake News Detection Using Machine Learning
SN - 978-989-758-777-1
AU - Fatima S.
AU - Sameer S.
AU - Kumar M.
AU - Inthiyaz S.
AU - Chand S.
AU - Pavan K.
PY - 2025
SP - 588
EP - 592
DO - 10.5220/0013886800004919
PB - SciTePress