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

Rahmanti, Annisa Ristya and Ningrum, Dina Nur Anggraini and Lazuardi, Lutfan and Yang, Hsuan-Chia and Li, Yu-Chuan(Jack) (2021) Social Media Data Analytics for Outbreak Risk Communication: Public Attention on the “New Normal” During the COVID-19 Pandemic in Indonesia. Computer Methods and Programs in Biomedicine, 205. ISSN 01692607

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Abstract

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. © 2021

Item Type: Article
Additional Information: Cited by: 26; All Open Access, Green Open Access
Uncontrolled Keywords: Attention; Communicable Disease Control; Communication; COVID-19; Data Science; Disease Outbreaks; Humans; Indonesia; Pandemics; SARS-CoV-2; Social Media; Behavioral research; Data Analytics; Public policy; Risk assessment; Risk perception; Social networking (online); COVID-19; Emotion analysis; Indonesia; New normal; Outbreak risk communication; Public perception; Risk communication; Sentiment analysis; Social media datum; Trend analysis; anger; anticipation; Article; coronavirus disease 2019; disgust; emotion; epidemic; fear; happiness; human; Indonesia; pandemic; public health campaign; public opinion; public policy; sadness; social media; social media analysis; social network; surprise; trust; attention; communicable disease control; epidemic; epidemiology; interpersonal communication; Sentiment analysis
Subjects: R Medicine > RB Biomedical Sciences
Divisions: Faculty of Medicine, Public Health and Nursing > Public Health and Nutrition
Depositing User: Sri JUNANDI
Date Deposited: 24 Sep 2024 08:16
Last Modified: 24 Sep 2024 08:16
URI: https://ir.lib.ugm.ac.id/id/eprint/4715

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