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Title: Machine Learning-Based Identifications of COVID-19 Fake News Using Biomedical Information Extraction
The spread of fake news related to COVID-19 is an infodemic that leads to a public health crisis. Therefore, detecting fake news is crucial for an effective management of the COVID-19 pandemic response. Studies have shown that machine learning models can detect COVID-19 fake news based on the content of news articles. However, the use of biomedical information, which is often featured in COVID-19 news, has not been explored in the development of these models. We present a novel approach for predicting COVID-19 fake news by leveraging biomedical information extraction (BioIE) in combination with machine learning models. We analyzed 1164 COVID-19 news articles and used advanced BioIE algorithms to extract 158 novel features. These features were then used to train 15 machine learning classifiers to predict COVID-19 fake news. Among the 15 classifiers, the random forest model achieved the best performance with an area under the ROC curve (AUC) of 0.882, which is 12.36% to 31.05% higher compared to models trained on traditional features. Furthermore, incorporating BioIE-based features improved the performance of a state-of-the-art multi-modality model (AUC 0.914 vs. 0.887). Our study suggests that incorporating biomedical information into fake news detection models improves their performance, and thus could be a valuable tool in the fight against the COVID-19 infodemic.  more » « less
Award ID(s):
1742517
PAR ID:
10484386
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
MPDI
Date Published:
Journal Name:
Big Data and Cognitive Computing
Volume:
7
Issue:
1
ISSN:
2504-2289
Page Range / eLocation ID:
46
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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