Understanding COVID-19 response by twitter users: A text analysis approach
COVID-19 outbreak has caused a high number of casualties and is an unprecedented public health emergency. Twitter has emerged as a major platform for public interactions, giving opportunity to researchers for understanding public response to the outbreak. The researchers analyzed 100,000 tweets with...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-08-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844022012828 |
| _version_ | 1818009965523107840 |
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| author | Digvijay Pandey Subodh Wairya Bandinee Pradhan Wangmo |
| author_facet | Digvijay Pandey Subodh Wairya Bandinee Pradhan Wangmo |
| author_sort | Digvijay Pandey |
| collection | DOAJ |
| description | COVID-19 outbreak has caused a high number of casualties and is an unprecedented public health emergency. Twitter has emerged as a major platform for public interactions, giving opportunity to researchers for understanding public response to the outbreak. The researchers analyzed 100,000 tweets with hashtags #coronavirus, #coronavirusoutbreak, #coronavirusPandemic, #COVID19, #COVID-19, #epitwitter, #ihavecorona, #StayHomeStaySafe, #TestTraceIsolate. Programming languages such as Python, Google NLP, and NVivo are used for sentiment analysis and thematic analysis. The result showed 29.61% tweets were attached to positive sentiments, 29.49% mixed sentiments, 23.23 % neutral sentiments and 18.069% negative sentiments. Popular keywords include “cases”, “home”, “people” and “help”. We identified “30” such topics and categorized them into “three” themes: Public Health, COVID-19 around the world and Number of Cases/Death. This study shows twitter data and NLP approach can be utilized for studies related to public discussion and sentiments during the COVID-19 outbreak. Real time analysis can help reduce the false messages and increase the efficiency in proving the right guidelines for people. |
| first_indexed | 2024-04-14T05:49:29Z |
| format | Article |
| id | doaj.art-72fc1b067b9548768bb0a4a51a2776c2 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-14T05:49:29Z |
| publishDate | 2022-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-72fc1b067b9548768bb0a4a51a2776c22022-12-22T02:09:10ZengElsevierHeliyon2405-84402022-08-0188e09994Understanding COVID-19 response by twitter users: A text analysis approachDigvijay Pandey0Subodh Wairya1Bandinee Pradhan2 Wangmo3Department of Electronics Engineering, Institute of Engineering and Technology, Lucknow, India; Corresponding author.Department of Electronics Engineering, Institute of Engineering and Technology, Lucknow, IndiaPDPU, Gandhinagar, IndiaSherubtse College, Royal University of Bhutan, BhutanCOVID-19 outbreak has caused a high number of casualties and is an unprecedented public health emergency. Twitter has emerged as a major platform for public interactions, giving opportunity to researchers for understanding public response to the outbreak. The researchers analyzed 100,000 tweets with hashtags #coronavirus, #coronavirusoutbreak, #coronavirusPandemic, #COVID19, #COVID-19, #epitwitter, #ihavecorona, #StayHomeStaySafe, #TestTraceIsolate. Programming languages such as Python, Google NLP, and NVivo are used for sentiment analysis and thematic analysis. The result showed 29.61% tweets were attached to positive sentiments, 29.49% mixed sentiments, 23.23 % neutral sentiments and 18.069% negative sentiments. Popular keywords include “cases”, “home”, “people” and “help”. We identified “30” such topics and categorized them into “three” themes: Public Health, COVID-19 around the world and Number of Cases/Death. This study shows twitter data and NLP approach can be utilized for studies related to public discussion and sentiments during the COVID-19 outbreak. Real time analysis can help reduce the false messages and increase the efficiency in proving the right guidelines for people.http://www.sciencedirect.com/science/article/pii/S2405844022012828COVID-19TwitterPublic healthPublic opinionSentiments |
| spellingShingle | Digvijay Pandey Subodh Wairya Bandinee Pradhan Wangmo Understanding COVID-19 response by twitter users: A text analysis approach Heliyon COVID-19 Public health Public opinion Sentiments |
| title | Understanding COVID-19 response by twitter users: A text analysis approach |
| title_full | Understanding COVID-19 response by twitter users: A text analysis approach |
| title_fullStr | Understanding COVID-19 response by twitter users: A text analysis approach |
| title_full_unstemmed | Understanding COVID-19 response by twitter users: A text analysis approach |
| title_short | Understanding COVID-19 response by twitter users: A text analysis approach |
| title_sort | understanding covid 19 response by twitter users a text analysis approach |
| topic | COVID-19 Public health Public opinion Sentiments |
| url | http://www.sciencedirect.com/science/article/pii/S2405844022012828 |
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