Social Media Data Analytics for Outbreak Risk Communication: Public Attention on the "New Normal" During the COVID-19 Pandemic in Indonesia

被引:17
作者
Rahmanti, Annisa Ristya [1 ,2 ,3 ]
Ningrum, Dina Nur Anggraini [1 ,2 ,4 ]
Lazuardi, Lutfan [3 ]
Yang, Hsuan-Chia [1 ,2 ,5 ]
Li, Yu-Chuan [1 ,2 ,5 ,6 ,7 ]
机构
[1] Taipei Med Univ, Coll Med Sci & Technol, Grad Inst Biomed Informat, Taipei, Taiwan
[2] Taipei Med Univ, Int Ctr Hlth Informat Technol ICHIT, Taipei, Taiwan
[3] Univ Gadjah Mada, Fac Med Publ Hlth & Nursing, Dept Hlth Policy Management, Yogyakarta, Indonesia
[4] Univ Negeri Semarang UNNES, Publ Hlth Dept, Semarang, Indonesia
[5] Taipei Med Univ, Wan Fang Hosp, Res Ctr Big Data & Meta Anal, Taipei, Taiwan
[6] Wan Fang Hosp, Dept Dermatol, Taipei, Taiwan
[7] Taipei Med Univ, TMU Res Ctr Canc Translat Med, Taipei, Taiwan
关键词
New normal; COVID-19; Outbreak risk communication; Twitter; Sentiment analysis;
D O I
10.1016/j.cmpb.2021.106083
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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. (c) 2021 Elsevier B.V. All rights reserved.
引用
收藏
页数:7
相关论文
共 32 条
[1]  
Ahmed W., 2017, The ethics of online research, V2, P79, DOI [DOI 10.1108/S2398-601820180000002004, 10.1108/S2398-601820180000002004, 10.1016/j.jocn.2005.03.017]
[2]  
[Anonymous], 2020, Quick Survey for Community Response for TG and Hijra
[3]  
[Anonymous], 2020, SUB DIREKTORAT PENYA
[4]   Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak [J].
Chew, Cynthia ;
Eysenbach, Gunther .
PLOS ONE, 2010, 5 (11)
[5]   Facebook and Twitter vaccine sentiment in response to measles outbreaks [J].
Deiner, Michael S. ;
Fathy, Cherie ;
Kim, Jessica ;
Niemeyer, Katherine ;
Ramirez, David ;
Ackley, Sarah F. ;
Liu, Fengchen ;
Lietman, Thomas M. ;
Porco, Travis C. .
HEALTH INFORMATICS JOURNAL, 2019, 25 (03) :1116-1132
[6]  
Du F., 2020, ASSESSMENT PUBLIC AT, DOI DOI 10.1101/2020.03.14
[7]  
Fahmi I., 2009, AUTOMATIC TERM RELAT
[8]  
Fahmi I., 2017, DRONE EMPRIT KONSEP
[9]  
Fahmi I., 2020, JEROAN DRONE EMPRIT
[10]  
Gamhewage Gaya., 2014, An introduction to risk communication