The emotions for COVID-19 vaccine: Insights from Twitter analytics about hesitancy and willingness for vaccination

被引:0
作者
Singh, Shiwangi [1 ]
Dhir, Sanjay [2 ]
Sushil [2 ]
机构
[1] Indian Inst Management, Ranchi, Jharkhand, India
[2] Indian Inst Technol Delhi, New Delhi, India
关键词
COVID-19; vaccine; Policy implications; Emotion classification; Hesitancy; Willingness; Sentiments; Vaccination program; SEMANTIC NETWORK ANALYSIS; SENTIMENT; MOVEMENT;
D O I
10.1016/j.jpolmod.2024.05.005
中图分类号
F [经济];
学科分类号
02 ;
摘要
The declaration by the World Health Organization and government-initiated actions by different countries for the COVID-19 vaccine have led to the rapid evolution of sentiments on various social media platforms. Real-time data related to vaccination has grown the need to anticipate the changes in vaccine uptake. Using Twitter dataset, the study models different emotions and their associated word. The emotions are majorly classified into hesitancy and willingness for vaccination. The study categorizes the tweets into pre-launch, post-launch, and booster doses of the COVID-19 vaccine. Based on comparative analysis, most sentiments were related to hesitancy for vaccination during pre-launch. In post-launch, the majority of sentiments were oriented towards willingness for vaccination. However, during the booster dose, the sentiments were oriented toward happy, adequate, and free emotions. Over the time period, the willingness of the COVID-19 vaccine has improved. The practitioners and policymakers can obtain real-time sentiments based on this approach and strategize the long-term vaccination policy for COVID-19 and other vaccination programs. (c) 2024 The Society for Policy Modeling. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
引用
收藏
页码:964 / 984
页数:21
相关论文
共 47 条
  • [1] Adarov Amat, 2022, J Policy Model, V44, P842, DOI 10.1016/j.jpolmod.2022.09.013
  • [2] Sentiment Analysis Using Common-Sense and Context Information
    Agarwal, Basant
    Mittal, Namita
    Bansal, Pooja
    Garg, Sonal
    [J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2015, 2015
  • [3] [Anonymous], 2005, Causal mapping for information systems and technology research, DOI 10.4018/978-1-59140-396-8.ch004
  • [4] Converting the maybes: Crucial for a successful COVID-19 vaccination strategy
    Attwell, Katie
    Lake, Joshua
    Sneddon, Joanne
    Gerrans, Paul
    Blyth, Chris
    Lee, Julie
    [J]. PLOS ONE, 2021, 16 (01):
  • [5] The link between the two epidemics provides an opportunity to remedy obesity while dealing with Covid-19
    Barrera, Emiliano Lopez
    Miljkovic, Dragan
    [J]. JOURNAL OF POLICY MODELING, 2022, 44 (02) : 280 - 297
  • [6] Benett S., 2012, Just How Big is Twitter in 2012
  • [7] Bonnevie Erika, 2021, Journal of Communication in Healthcare, V14, P12, DOI 10.1080/17538068.2020.1858222
  • [8] Borgatti SP., 2002, Harv. MA: Analytic Technol, V6, P12, DOI [DOI 10.1111/J.1439-0310.2009.01613.X, 10.1111/j.1439-0310.2009.01613.x]
  • [9] Through the lens of ethnicity: Semantic network and thematic analyses of United Airlines' dragging crisis
    Cho, Moonhee
    Xiong, Ying
    Boatwright, Brandon
    [J]. PUBLIC RELATIONS REVIEW, 2021, 47 (01)
  • [10] Using social network and semantic analysis to analyze online travel forums and forecast tourism demand
    Colladon, Andrea Fronzetti
    Guardabascio, Barbara
    Innarella, Rosy
    [J]. DECISION SUPPORT SYSTEMS, 2019, 123