Novel approach for predicting fake news stance detection using large word embedding blending and customized CNN model

The proliferation of fake news is one of the major problems that causes personal and societal harm. In today's fast-paced digital age, misinformation spreads rapidly, often leaving individuals without the time to verify the authenticity of the information. This can cause irreparable damage to p...

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Bibliographic Details
Published in:PloS one 2024-12, Vol.19 (12), p.e0314174
Main Author: Altamimi, Abdulaziz
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Computational linguistics
Customization
Damage detection
Datasets
Deception
Digital Age
Disinformation
Embedding
Ensemble learning
False information
Humans
Identification and classification
Language processing
Machine learning
Methods
Natural language interfaces
Neural networks
Neural Networks, Computer
News
Social Media
Social networks
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Summary:The proliferation of fake news is one of the major problems that causes personal and societal harm. In today's fast-paced digital age, misinformation spreads rapidly, often leaving individuals without the time to verify the authenticity of the information. This can cause irreparable damage to personal reputations and organizational credibility. Thus, instigated by the quintessential necessity, there is a dire need to construct a framework for the automatic detection and identification of fake news at its inception. This research presents a novel approach that leverages a combination of three popular word embeddings (FastText, FastText-Subword, and GloVe) integrated with a customized convolutional neural-network(CNN) to classify fake news accurately. The proposed model was tested against the Fake News Challenge dataset. Hundreds of word vector features were generated from the combined embedding and then managed with PCA and significant features were extracted. The proposed model gives an accuracy of 94.58%, 95.35% precision, 97.29% recall, and an F1 score of 96.11%. The proposed framework's robustness is demonstrated when compared with other machine, deep, and ensemble learning approaches, showing superior performance. Furthermore, the effectiveness of the model is validated on an independent Arabic Fake News dataset.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0314174