Combat COVID-19 infodemic using explainable natural language processing models

Misinformation of COVID-19 is prevalent on social media as the pandemic unfolds, and the associated risks are extremely high. Thus, it is critical to detect and combat such misinformation. Recently, deep learning models using natural language processing techniques, such as BERT (Bidirectional Encode...

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Bibliographic Details
Published in:	Information processing & management Vol. 58; no. 4; p. 102569
Main Authors:	Ayoub, Jackie, Yang, X. Jessie, Zhou, Feng
Format:	Journal Article
Language:	English
Published:	England Elsevier Ltd 01.07.2021 Elsevier Science Ltd
Subjects:	Accuracy BERT Between-subjects design Bidirectionality Coders Coronaviruses COVID-19 Datasets Deep learning DistilBERT Effectiveness Efficiency False information Language Language acquisition Machine learning Misinformation Misinformation detection Model testing Natural language processing Pandemics SHAP Social media Social networks Trust COVID-19 SHAP BERT Misinformation detection DistilBERT Trust
ISSN:	0306-4573, 1873-5371, 0306-4573
Online Access:	Get full text
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