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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Vydané v:	Information processing & management Ročník 58; číslo 4; s. 102569
Hlavní autori:	Ayoub, Jackie, Yang, X. Jessie, Zhou, Feng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England Elsevier Ltd 01.07.2021 Elsevier Science Ltd
Predmet:	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
On-line prístup:	Získať plný text
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