AENeT: an attention-enabled neural architecture for fake news detection using contextual features

In the current era of social media, the popularity of smartphones and social media platforms has increased exponentially. Through these electronic media, fake news has been rising rapidly with the advent of new sources of information, which are highly unreliable. Checking off a particular news artic...

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Veröffentlicht in:Neural computing & applications Jg. 34; H. 1; S. 771 - 782
Hauptverfasser: Jain, Vidit, Kaliyar, Rohit Kumar, Goswami, Anurag, Narang, Pratik, Sharma, Yashvardhan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.01.2022
Springer Nature B.V
Schlagworte:
Artificial Intelligence
Classification
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer architecture
Computer Science
Data Mining and Knowledge Discovery
Datasets
Deep learning
Digital media
Embedding
Image Processing and Computer Vision
Metadata
Neural networks
News
Original
Original Article
Probability and Statistics in Computer Science
Search engines
Semantics
Smartphones
Social networks
Social research
Deep learning
Contextualized Features
Neural Network
Fake News
Social Media
ISSN:0941-0643, 1433-3058
Online-Zugang:Volltext
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Zusammenfassung:In the current era of social media, the popularity of smartphones and social media platforms has increased exponentially. Through these electronic media, fake news has been rising rapidly with the advent of new sources of information, which are highly unreliable. Checking off a particular news article is genuine or fake is not easy for any end user. Search engines like Google are also not capable of telling about the fakeness of any news article due to its restriction with limited query keywords. In this paper, our end goal is to design an efficient deep learning model to detect the degree of fakeness in a news statement. We propose a simple network architecture that combines the use of contextual embedding as word embedding and uses attention mechanisms with relevant metadata available. The efficacy and efficiency of our models are demonstrated on several real-world datasets. Our model achieved 46.36% accuracy on the LIAR dataset, which outperforms the current state of the art by 1.49%.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-021-06450-4