DeepFND: an ensemble-based deep learning approach for the optimization and improvement of fake news detection in digital platform
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F23%3A50020963" target="_blank" >RIV/62690094:18470/23:50020963 - isvavai.cz</a>
Result on the web
<a href="https://peerj.com/articles/cs-1666/" target="_blank" >https://peerj.com/articles/cs-1666/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.7717/peerj-cs.1666" target="_blank" >10.7717/peerj-cs.1666</a>
Alternative languages
Result language
angličtina
Original language name
DeepFND: an ensemble-based deep learning approach for the optimization and improvement of fake news detection in digital platform
Original language description
Early identification of false news is now essential to save lives from the dangers posed by its spread. People keep sharing false information even after it has been debunked. Those responsible for spreading misleading information in the first place should face the consequences, not the victims of their actions. Understanding how misinformation travels and how to stop it is an absolute need for society and government. Consequently, the necessity to identify false news from genuine stories has emerged with the rise of these social media platforms. One of the tough issues of conventional methodologies is identifying false news. In recent years, neural network models' performance has surpassed that of classic machine learning approaches because of their superior feature extraction. This research presents Deep learningbased Fake News Detection (DeepFND). This technique has Visual Geometry Group 19 (VGG-19) and Bidirectional Long Short Term Memory (Bi-LSTM) ensemble models for identifying misinformation spread through social media. This system uses an ensemble deep learning (DL) strategy to extract characteristics from the article's text and photos. The joint feature extractor and the attention modules are used with an ensemble approach, including pre-training and fine-tuning phases. In this article, we utilized a unique customized loss function. In this research, we look at methods for detecting bogus news on the internet without human intervention. We used the Weibo, liar, PHEME, fake and real news, and Buzzfeed datasets to analyze fake and real news. Multiple methods for identifying fake news are compared and contrasted. Precision procedures have been used to calculate the proposed model's output. The model's 99.88% accuracy is better than expected.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
PEERJ COMPUTER SCIENCE
ISSN
2376-5992
e-ISSN
2376-5992
Volume of the periodical
9
Issue of the periodical within the volume
December
Country of publishing house
GB - UNITED KINGDOM
Number of pages
25
Pages from-to
"Article Number: e1666"
UT code for WoS article
001120971000001
EID of the result in the Scopus database
—