Enhancing Fake News Detection: A Multimodal Approach Integrating Machine Learning

N. Ramesh Reddy., P. Dinesh., N. Venkateswaralu., Dr. Shoba Rani., Dr. A. Vinodh Kumar. and Dr. Rekha
Additional contact information
N. Ramesh Reddy.: Associate Professor Dr. M.G.R. Educational and Research Institute, Madhuravoyal, Chennai, Tamil Nadu - 600095
P. Dinesh.: Associate Professor Dr. M.G.R. Educational and Research Institute, Madhuravoyal, Chennai, Tamil Nadu - 600095
N. Venkateswaralu.: Associate Professor Dr. M.G.R. Educational and Research Institute, Madhuravoyal, Chennai, Tamil Nadu - 600095
Dr. Shoba Rani.: Associate Professor Dr. M.G.R. Educational and Research Institute, Madhuravoyal, Chennai, Tamil Nadu - 600095
Dr. A. Vinodh Kumar.: Associate Professor Dr. M.G.R. Educational and Research Institute, Madhuravoyal, Chennai, Tamil Nadu - 600095
Dr. Rekha: Associate Professor Dr. M.G.R. Educational and Research Institute, Madhuravoyal, Chennai, Tamil Nadu - 600095

International Journal of Latest Technology in Engineering, Management & Applied Science, 2025, vol. 14, issue 3, 272-281

Abstract: Fake news on digital platforms is a significant threat to information integrity, shaping public opinion and eroding trust in media sources. This project proposes a comprehensive approach to fake news detection using a [2]multimodal framework that combines [5]Machine learning techniques for text, images, and metadata. Unlike our approach, existing systems use [10]convolutional neural Networks (CNNs) for image analysis and [12]natural language processing (NLP) models for text analysis and metadata to get a holistic view of news content. To build a robust detection mechanism, we use a diverse dataset with real and fake news articles, manipulated images, and misleading [4]metadata. The results show a significant improvement in detection [8]accuracy over single-modal models, with high precision and [8]recall. This project not only contributes to the field of fake news detection but also highlights the importance of ethical considerations in [3]AI systems. Future work will be to extend the model to detect deepfakes and misinformation in multimedia content and apply it to real-world scenarios.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.ijltemas.in/DigitalLibrary/Vol.14Issue3/272-281.pdf (application/pdf)
https://www.ijltemas.in/papers/volume-14-issue-3/272-281.html (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bjb:journl:v:14:y:2025:i:3:p:272-281

Access Statistics for this article

International Journal of Latest Technology in Engineering, Management & Applied Science is currently edited by Dr. Pawan Verma

More articles in International Journal of Latest Technology in Engineering, Management & Applied Science from International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS)
Bibliographic data for series maintained by Dr. Pawan Verma ().