Combating the Infodemic: A Chinese Infodemic Dataset for Misinformation Identification

Misinformation posted on social media during COVID-19 is one main example of infodemic data. This phenomenon was prominent in China when COVID-19 happened at the beginning. While a lot of data can be collected from various social media platforms, publicly available infodemic detection data remains r...

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Vydáno v:Healthcare (Basel) Ročník 9; číslo 9; s. 1094
Hlavní autoři: Luo, Jia, Xue, Rui, Hu, Jinglu, El Baz, Didier
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 24.08.2021
MDPI
Témata:
Annotations
Artificial Intelligence
Computer Science
Coronaviruses
COVID-19
Credibility
Data collection
Datasets
Deep learning
Disease transmission
Distributed, Parallel, and Cluster Computing
Experiments
False information
Gossip
Machine Learning
Pandemics
Social and Information Networks
Social networks
China
COVID-19
deep learning
misinformation identification
infodemic data
ISSN:2227-9032, 2227-9032
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Shrnutí:Misinformation posted on social media during COVID-19 is one main example of infodemic data. This phenomenon was prominent in China when COVID-19 happened at the beginning. While a lot of data can be collected from various social media platforms, publicly available infodemic detection data remains rare and is not easy to construct manually. Therefore, instead of developing techniques for infodemic detection, this paper aims at constructing a Chinese infodemic dataset, “infodemic 2019”, by collecting widely spread Chinese infodemic during the COVID-19 outbreak. Each record is labeled as true, false or questionable. After a four-time adjustment, the original imbalanced dataset is converted into a balanced dataset by exploring the properties of the collected records. The final labels achieve high intercoder reliability with healthcare workers’ annotations and the high-frequency words show a strong relationship between the proposed dataset and pandemic diseases. Finally, numerical experiments are carried out with RNN, CNN and fastText. All of them achieve reasonable performance and present baselines for future works.
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ISSN:2227-9032
2227-9032
DOI:10.3390/healthcare9091094