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Social Media Data Analytics for Outbreak Risk Communication: Public Attention on the “New Normal” During the COVID-19 Pandemic in Indonesia
BACKGROUND: After two months of implementing a partial lockdown, the Indonesian government had announced the “New Normal” policy to prevent a further economic crash in the country. This policy received many critics, as Indonesia still experiencing a fluctuated number of infected cases. Understanding...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9188283/ https://www.ncbi.nlm.nih.gov/pubmed/33906012 http://dx.doi.org/10.1016/j.cmpb.2021.106083 |
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author | Rahmanti, Annisa Ristya Ningrum, Dina Nur Anggraini Lazuardi, Lutfan Yang, Hsuan-Chia Li, Yu-Chuan(Jack) |
author_facet | Rahmanti, Annisa Ristya Ningrum, Dina Nur Anggraini Lazuardi, Lutfan Yang, Hsuan-Chia Li, Yu-Chuan(Jack) |
author_sort | Rahmanti, Annisa Ristya |
collection | PubMed |
description | BACKGROUND: After two months of implementing a partial lockdown, the Indonesian government had announced the “New Normal” policy to prevent a further economic crash in the country. This policy received many critics, as Indonesia still experiencing a fluctuated number of infected cases. Understanding public perception through effective risk communication can assist the government in relaying an appropriate message to improve people's compliance and to avoid further disease spread. OBJECTIVE: This study observed how risk communication using social media platforms like Twitter could be adopted to measure public attention on COVID-19 related issues “New Normal”. METHOD: From May 21 to June 18, 2020, we archived all tweets related to COVID-19 containing keywords: “#NewNormal”, and “New Normal” using Drone Emprit Academy (DEA) engine. DEA search API collected all requested tweets and described the cumulative tweets for trend analysis, word segmentation, and word frequency. We further analyzed the public perception using sentiment analysis and identified the predominant tweets using emotion analysis. RESULT: We collected 284,216 tweets from 137,057 active users. From the trend analysis, we observed three stages of the changing trend of the public's attention on the “New Normal”. Results from the sentiment analysis indicate that more than half of the population (52%) had a “positive” sentiment towards the “New Normal” issues while only 41% of them had a “negative” perception. Our study also demonstrated the public's sentiment trend has gradually shifted from “negative” to “positive” due to the influence of both the government actions and the spread of the disease. A more detailed analysis of the emotion analysis showed that the majority of the public emotions (77.6%) relied on the emotion of “trust”, “anticipation”, and “joy”. Meanwhile, people were also surprised (8.62%) that the Indonesian government progressed to the “New Normal” concept despite a fluctuating number of cases. CONCLUSION: Our findings offer an opportunity for the government to use Twitter in the process of quick decision-making and policy evaluation during uncertain times in response to the COVID-19 pandemic. |
format | Online Article Text |
id | pubmed-9188283 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91882832022-06-13 Social Media Data Analytics for Outbreak Risk Communication: Public Attention on the “New Normal” During the COVID-19 Pandemic in Indonesia Rahmanti, Annisa Ristya Ningrum, Dina Nur Anggraini Lazuardi, Lutfan Yang, Hsuan-Chia Li, Yu-Chuan(Jack) Comput Methods Programs Biomed Article BACKGROUND: After two months of implementing a partial lockdown, the Indonesian government had announced the “New Normal” policy to prevent a further economic crash in the country. This policy received many critics, as Indonesia still experiencing a fluctuated number of infected cases. Understanding public perception through effective risk communication can assist the government in relaying an appropriate message to improve people's compliance and to avoid further disease spread. OBJECTIVE: This study observed how risk communication using social media platforms like Twitter could be adopted to measure public attention on COVID-19 related issues “New Normal”. METHOD: From May 21 to June 18, 2020, we archived all tweets related to COVID-19 containing keywords: “#NewNormal”, and “New Normal” using Drone Emprit Academy (DEA) engine. DEA search API collected all requested tweets and described the cumulative tweets for trend analysis, word segmentation, and word frequency. We further analyzed the public perception using sentiment analysis and identified the predominant tweets using emotion analysis. RESULT: We collected 284,216 tweets from 137,057 active users. From the trend analysis, we observed three stages of the changing trend of the public's attention on the “New Normal”. Results from the sentiment analysis indicate that more than half of the population (52%) had a “positive” sentiment towards the “New Normal” issues while only 41% of them had a “negative” perception. Our study also demonstrated the public's sentiment trend has gradually shifted from “negative” to “positive” due to the influence of both the government actions and the spread of the disease. A more detailed analysis of the emotion analysis showed that the majority of the public emotions (77.6%) relied on the emotion of “trust”, “anticipation”, and “joy”. Meanwhile, people were also surprised (8.62%) that the Indonesian government progressed to the “New Normal” concept despite a fluctuating number of cases. CONCLUSION: Our findings offer an opportunity for the government to use Twitter in the process of quick decision-making and policy evaluation during uncertain times in response to the COVID-19 pandemic. Elsevier B.V. 2021-06 2021-04-06 /pmc/articles/PMC9188283/ /pubmed/33906012 http://dx.doi.org/10.1016/j.cmpb.2021.106083 Text en © 2021 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Rahmanti, Annisa Ristya Ningrum, Dina Nur Anggraini Lazuardi, Lutfan Yang, Hsuan-Chia Li, Yu-Chuan(Jack) Social Media Data Analytics for Outbreak Risk Communication: Public Attention on the “New Normal” During the COVID-19 Pandemic in Indonesia |
title | Social Media Data Analytics for Outbreak Risk Communication: Public Attention on the “New Normal” During the COVID-19 Pandemic in Indonesia |
title_full | Social Media Data Analytics for Outbreak Risk Communication: Public Attention on the “New Normal” During the COVID-19 Pandemic in Indonesia |
title_fullStr | Social Media Data Analytics for Outbreak Risk Communication: Public Attention on the “New Normal” During the COVID-19 Pandemic in Indonesia |
title_full_unstemmed | Social Media Data Analytics for Outbreak Risk Communication: Public Attention on the “New Normal” During the COVID-19 Pandemic in Indonesia |
title_short | Social Media Data Analytics for Outbreak Risk Communication: Public Attention on the “New Normal” During the COVID-19 Pandemic in Indonesia |
title_sort | social media data analytics for outbreak risk communication: public attention on the “new normal” during the covid-19 pandemic in indonesia |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9188283/ https://www.ncbi.nlm.nih.gov/pubmed/33906012 http://dx.doi.org/10.1016/j.cmpb.2021.106083 |
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