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Spatio-temporal approach for classification of COVID-19 pandemic fake news

Spatio-temporal approach for classification of COVID-19 pandemic fake news The spread of Fake News during this global pandemic COVID-19 has dangerous consequences on economy and health of public. From origin of virus, spread, self-medication to hoaxes on vaccination, it created more panic than the fatality of the virus. For better infodemic preparedness and control, it is necessary to mitigate fear among people, manage rumours, and dispel misinformation. A survey on Fake News during COVID-19 was made by Poynter Fact Check institute. It stated that major chunk of the fake news on COVID-19 originated majorly in Brazil, India, Spain, and the United States. Fake news menace is severe in countries where the trust on online media is high such as Brazil, Kenya and South Africa. Based on these observations, this study provides preliminary insight on the co-relation of the spatial and temporal meta-information of the news like the news source country, the name of the countries specified in the news, and date of publish of news to the credibility of news. The main contribution of this study is to analyse the impact of spatial and temporal information features for classification of fake news, which to the best of our knowledge has not been explored yet. Also, these features are directly not available in any news article available online. Hence, these features are handcrafted. Meta-data of the news article such as origin of news is considered. Additional spatial information is extracted from the news article using NER tagging. Temporal information such as date of origin of news is given as an input to the LSTM model. These features are given as an input to Long Short-Term Memory (LSTM) model along with GloVe vectors and word length vector. A comparative analysis for accuracy is tested of the models with and without spatial and temporal information. The model with spatial and temporal information has achieved noteworthy results in fake news detection. To ensure the quality of prediction, various model parameters have been tuned and recorded for the best results possible. In addition to accuracy, the spatial and temporal information for fake news detection offers several other important implications for government and policy makers that will be instrumental in simulating future research on this subject. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Social Network Analysis and Mining Springer Journals

Spatio-temporal approach for classification of COVID-19 pandemic fake news

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Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022
ISSN
1869-5450
eISSN
1869-5469
DOI
10.1007/s13278-022-00887-8
Publisher site
See Article on Publisher Site

Abstract

The spread of Fake News during this global pandemic COVID-19 has dangerous consequences on economy and health of public. From origin of virus, spread, self-medication to hoaxes on vaccination, it created more panic than the fatality of the virus. For better infodemic preparedness and control, it is necessary to mitigate fear among people, manage rumours, and dispel misinformation. A survey on Fake News during COVID-19 was made by Poynter Fact Check institute. It stated that major chunk of the fake news on COVID-19 originated majorly in Brazil, India, Spain, and the United States. Fake news menace is severe in countries where the trust on online media is high such as Brazil, Kenya and South Africa. Based on these observations, this study provides preliminary insight on the co-relation of the spatial and temporal meta-information of the news like the news source country, the name of the countries specified in the news, and date of publish of news to the credibility of news. The main contribution of this study is to analyse the impact of spatial and temporal information features for classification of fake news, which to the best of our knowledge has not been explored yet. Also, these features are directly not available in any news article available online. Hence, these features are handcrafted. Meta-data of the news article such as origin of news is considered. Additional spatial information is extracted from the news article using NER tagging. Temporal information such as date of origin of news is given as an input to the LSTM model. These features are given as an input to Long Short-Term Memory (LSTM) model along with GloVe vectors and word length vector. A comparative analysis for accuracy is tested of the models with and without spatial and temporal information. The model with spatial and temporal information has achieved noteworthy results in fake news detection. To ensure the quality of prediction, various model parameters have been tuned and recorded for the best results possible. In addition to accuracy, the spatial and temporal information for fake news detection offers several other important implications for government and policy makers that will be instrumental in simulating future research on this subject.

Journal

Social Network Analysis and MiningSpringer Journals

Published: Dec 1, 2022

Keywords: Spatio-temporal information; COVID-19; Infodemic; Fake news detection; Deep learning

References