Social media sentiment analysis to monitor the performance of vaccination coverage during the early phase of the national COVID-19 vaccine rollout
Background and objective: Social media sentiment analysis based on Twitter data can facilitate real-time monitoring of COVID-19 vaccine-related concerns. Thus, the governments can adopt proactive measures to address misinformation and inappropriate behaviors surrounding the COVID-19 vaccine, threate...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022
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| Subjects: | |
| Online Access: | https://repository.ugm.ac.id/282674/1/204.pdf |
| _version_ | 1826050534546604032 |
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| author | Rahmanti, Annisa Ristya Chien, Chia-Hui Nursetyo, Aldilas Achmad Husnayain, Atina Wiratama, Bayu Satria Fuad, Anis Yang, Hsuan-Chia Li, Yu-Chuan Jack |
| author_facet | Rahmanti, Annisa Ristya Chien, Chia-Hui Nursetyo, Aldilas Achmad Husnayain, Atina Wiratama, Bayu Satria Fuad, Anis Yang, Hsuan-Chia Li, Yu-Chuan Jack |
| author_sort | Rahmanti, Annisa Ristya |
| collection | UGM |
| description | Background and objective: Social media sentiment analysis based on Twitter data can facilitate real-time monitoring of COVID-19 vaccine-related concerns. Thus, the governments can adopt proactive measures to address misinformation and inappropriate behaviors surrounding the COVID-19 vaccine, threatening the success of the national vaccination campaign. This study aims to identify the correlation between COVID19 vaccine sentiments expressed on Twitter and COVID-19 vaccination coverage, case increase, and case
fatality rate in Indonesia. Methods: We retrieved COVID-19 vaccine-related tweets collected from Indonesian Twitter users between October 15, 2020, to April 12, 2021, using Drone Emprit Academic (DEA) platform. We collected the daily
trend of COVID-19 vaccine coverage and the rate of case increase and case fatality from the Ministry of Health (MoH) official website and the KawalCOVID19 database, respectively. We identified the public sentiments, emotions, word usage, and trend of all filtered tweets 90 days before and after the national vaccination rollout in Indonesia.
Results: Using a total of 555,892 COVID-19 vaccine-related tweets, we observed the negative sentiments outnumbered positive sentiments for 59 days (65.50%), with the predominant emotion of anticipation among 90 days of the beginning of the study period. However, after the vaccination rollout, the positive sentiments outnumbered negative sentiments for 56 days (62.20%) with the growth of trust emotion, which is consistent with the positive appeals of the recent news about COVID-19 vaccine safety and the
government’s proactive risk communication. In addition, there was a statistically significant trend of vaccination sentiment scores, which strongly correlated with the increase of vaccination coverage (r = 0.71,P<.0001 both first and second doses) and the decreasing of case increase rate (r = -0.70, P<.0001) and case fatality rate (r = -0.74, P<.0001).Conclusions: Our results highlight the utility of social media sentiment analysis as government communication strategies to build public trust, affecting individual willingness to get vaccinated. This finding will be useful for countries to identify and develop strategies for speed up the vaccination rate by monitoring the dynamic netizens’ reactions and expression in social media, especially Twitter, using sentiment analysis. |
| first_indexed | 2024-03-14T00:05:30Z |
| format | Article |
| id | oai:generic.eprints.org:282674 |
| institution | Universiti Gadjah Mada |
| language | English |
| last_indexed | 2024-03-14T00:05:30Z |
| publishDate | 2022 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oai:generic.eprints.org:2826742023-11-16T06:25:59Z https://repository.ugm.ac.id/282674/ Social media sentiment analysis to monitor the performance of vaccination coverage during the early phase of the national COVID-19 vaccine rollout Rahmanti, Annisa Ristya Chien, Chia-Hui Nursetyo, Aldilas Achmad Husnayain, Atina Wiratama, Bayu Satria Fuad, Anis Yang, Hsuan-Chia Li, Yu-Chuan Jack Clinical Chemistry (diagnostics) Background and objective: Social media sentiment analysis based on Twitter data can facilitate real-time monitoring of COVID-19 vaccine-related concerns. Thus, the governments can adopt proactive measures to address misinformation and inappropriate behaviors surrounding the COVID-19 vaccine, threatening the success of the national vaccination campaign. This study aims to identify the correlation between COVID19 vaccine sentiments expressed on Twitter and COVID-19 vaccination coverage, case increase, and case fatality rate in Indonesia. Methods: We retrieved COVID-19 vaccine-related tweets collected from Indonesian Twitter users between October 15, 2020, to April 12, 2021, using Drone Emprit Academic (DEA) platform. We collected the daily trend of COVID-19 vaccine coverage and the rate of case increase and case fatality from the Ministry of Health (MoH) official website and the KawalCOVID19 database, respectively. We identified the public sentiments, emotions, word usage, and trend of all filtered tweets 90 days before and after the national vaccination rollout in Indonesia. Results: Using a total of 555,892 COVID-19 vaccine-related tweets, we observed the negative sentiments outnumbered positive sentiments for 59 days (65.50%), with the predominant emotion of anticipation among 90 days of the beginning of the study period. However, after the vaccination rollout, the positive sentiments outnumbered negative sentiments for 56 days (62.20%) with the growth of trust emotion, which is consistent with the positive appeals of the recent news about COVID-19 vaccine safety and the government’s proactive risk communication. In addition, there was a statistically significant trend of vaccination sentiment scores, which strongly correlated with the increase of vaccination coverage (r = 0.71,P<.0001 both first and second doses) and the decreasing of case increase rate (r = -0.70, P<.0001) and case fatality rate (r = -0.74, P<.0001).Conclusions: Our results highlight the utility of social media sentiment analysis as government communication strategies to build public trust, affecting individual willingness to get vaccinated. This finding will be useful for countries to identify and develop strategies for speed up the vaccination rate by monitoring the dynamic netizens’ reactions and expression in social media, especially Twitter, using sentiment analysis. Elsevier 2022-06 Article PeerReviewed application/pdf en https://repository.ugm.ac.id/282674/1/204.pdf Rahmanti, Annisa Ristya and Chien, Chia-Hui and Nursetyo, Aldilas Achmad and Husnayain, Atina and Wiratama, Bayu Satria and Fuad, Anis and Yang, Hsuan-Chia and Li, Yu-Chuan Jack (2022) Social media sentiment analysis to monitor the performance of vaccination coverage during the early phase of the national COVID-19 vaccine rollout. Computer Methods and Programs in Biomedicine, 221 (6). p. 106838. ISSN 1872-7565 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9045866/pdf/main.pdf 10.1016/j.cmpb.2022.106838 |
| spellingShingle | Clinical Chemistry (diagnostics) Rahmanti, Annisa Ristya Chien, Chia-Hui Nursetyo, Aldilas Achmad Husnayain, Atina Wiratama, Bayu Satria Fuad, Anis Yang, Hsuan-Chia Li, Yu-Chuan Jack Social media sentiment analysis to monitor the performance of vaccination coverage during the early phase of the national COVID-19 vaccine rollout |
| title | Social media sentiment analysis to monitor the performance of vaccination coverage during the early phase of the national COVID-19 vaccine rollout |
| title_full | Social media sentiment analysis to monitor the performance of vaccination coverage during the early phase of the national COVID-19 vaccine rollout |
| title_fullStr | Social media sentiment analysis to monitor the performance of vaccination coverage during the early phase of the national COVID-19 vaccine rollout |
| title_full_unstemmed | Social media sentiment analysis to monitor the performance of vaccination coverage during the early phase of the national COVID-19 vaccine rollout |
| title_short | Social media sentiment analysis to monitor the performance of vaccination coverage during the early phase of the national COVID-19 vaccine rollout |
| title_sort | social media sentiment analysis to monitor the performance of vaccination coverage during the early phase of the national covid 19 vaccine rollout |
| topic | Clinical Chemistry (diagnostics) |
| url | https://repository.ugm.ac.id/282674/1/204.pdf |
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