Journal article 34 views 6 downloads
A Multimodal Ensemble-Based Framework for Detecting Fake News Using Visual and Textual Features
,
Hongying Zan,
Arifa Javed ,
Muhammad Sohail,
Orken Mamyrbayev ,
Zhanibek Turysbek ,
Hassan Eshkiki ,
Fabio Caraffini
Mathematics, Volume: 14, Issue: 2, Start page: 360
Swansea University Authors:
Hassan Eshkiki , Fabio Caraffini
-
PDF | Version of Record
© 2026 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Download (15.79MB)
DOI (Published version): 10.3390/math14020360
Abstract
Detecting fake news is essential in natural language processing to verify news authenticity and prevent misinformation-driven social, political, and economic disruptions targeting specific groups. A major challenge in multimodal fake news detection is effectively integrating textual and visual modal...
| Published in: | Mathematics |
|---|---|
| ISSN: | 2227-7390 |
| Published: |
MDPI AG
2026
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa71351 |
| Abstract: |
Detecting fake news is essential in natural language processing to verify news authenticity and prevent misinformation-driven social, political, and economic disruptions targeting specific groups. A major challenge in multimodal fake news detection is effectively integrating textual and visual modalities, as semantic gaps and contextual variations between images and text complicate alignment, interpretation, and the detection of subtle or blatant inconsistencies. To enhance accuracy in fake news detection, this article introduces an ensemble-based framework that integrates textual and visual data using ViLBERT’s two-stream architecture, incorporates VADER sentiment analysis to detect emotional language, and uses Image–Text Contextual Similarity to identify mismatches between visual and textual elements. These features are processed through the Bi-GRU classifier, Transformer-XL, DistilBERT, and XLNet, combined via a stacked ensemble method with soft voting, culminating in a T5 metaclassifier that predicts the outcome for robustness. Results on the Fakeddit and Weibo benchmarking datasets show that our method outperforms state-of-the-art models, achieving up to 96% and 94% accuracy in fake news detection, respectively. This study highlights the necessity for advanced multimodal fake news detection systems to address the increasing complexity of misinformation and offers a promising solution. |
|---|---|
| Keywords: |
fake news detection; NLP; sentiment analysis; transformers; deep learning |
| College: |
Faculty of Science and Engineering |
| Issue: |
2 |
| Start Page: |
360 |