Infoveillance of the Croatian Online Media During the COVID-19 Pandemic: One-Year Longitudinal Study Using Natural Language Processing

BackgroundOnline media play an important role in public health emergencies and serve as essential communication platforms. Infoveillance of online media during the COVID-19 pandemic is an important step toward gaining a better understanding of crisis communication. ObjectiveThe goal of this study wa...

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Main Authors: Slobodan Beliga (Author), Sanda Martinčić-Ipšić (Author), Mihaela Matešić (Author), Irena Petrijevčanin Vuksanović (Author), Ana Meštrović (Author)
Format: Book
Published: JMIR Publications, 2021-12-01T00:00:00Z.
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042 |a dc 
100 1 0 |a Slobodan Beliga  |e author 
700 1 0 |a Sanda Martinčić-Ipšić  |e author 
700 1 0 |a Mihaela Matešić  |e author 
700 1 0 |a Irena Petrijevčanin Vuksanović  |e author 
700 1 0 |a Ana Meštrović  |e author 
245 0 0 |a Infoveillance of the Croatian Online Media During the COVID-19 Pandemic: One-Year Longitudinal Study Using Natural Language Processing 
260 |b JMIR Publications,   |c 2021-12-01T00:00:00Z. 
500 |a 2369-2960 
500 |a 10.2196/31540 
520 |a BackgroundOnline media play an important role in public health emergencies and serve as essential communication platforms. Infoveillance of online media during the COVID-19 pandemic is an important step toward gaining a better understanding of crisis communication. ObjectiveThe goal of this study was to perform a longitudinal analysis of the COVID-19-related content on online media based on natural language processing. MethodsWe collected a data set of news articles published by Croatian online media during the first 13 months of the pandemic. First, we tested the correlations between the number of articles and the number of new daily COVID-19 cases. Second, we analyzed the content by extracting the most frequent terms and applied the Jaccard similarity coefficient. Third, we compared the occurrence of the pandemic-related terms during the two waves of the pandemic. Finally, we applied named entity recognition to extract the most frequent entities and tracked the dynamics of changes during the observation period. ResultsThe results showed no significant correlation between the number of articles and the number of new daily COVID-19 cases. Furthermore, there were high overlaps in the terminology used in all articles published during the pandemic with a slight shift in the pandemic-related terms between the first and the second waves. Finally, the findings indicate that the most influential entities have lower overlaps for the identified people and higher overlaps for locations and institutions. ConclusionsOur study shows that online media have a prompt response to the pandemic with a large number of COVID-19-related articles. There was a high overlap in the frequently used terms across the first 13 months, which may indicate the narrow focus of reporting in certain periods. However, the pandemic-related terminology is well-covered. 
546 |a EN 
690 |a Public aspects of medicine 
690 |a RA1-1270 
655 7 |a article  |2 local 
786 0 |n JMIR Public Health and Surveillance, Vol 7, Iss 12, p e31540 (2021) 
787 0 |n https://publichealth.jmir.org/2021/12/e31540 
787 0 |n https://doaj.org/toc/2369-2960 
856 4 1 |u https://doaj.org/article/eec3a7866a894b2fac6dfb0c1bda3d30  |z Connect to this object online.