COVID-19 vaccine sensing: Sentiment analysis and subject distillation from twitter data

Han, Xu; Liu, Ruixin; Luo, Ziling; Xu, Minghua

doi:10.1016/j.teler.2022.100016

Cited by 12 publications

(9 citation statements)

References 33 publications

(28 reference statements)

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“…Due to the limited stock of vaccine numbers, cold chain storage, vaccinator, etc., the vaccination program was faster in urban areas than in rural areas, even in Java (Arifin & Anas, 2021). The public sentiment about the vaccine used in Indonesian COVID-19 vaccination program on its early implementation has become a challenge for the government (Xu et al, 2022). Data from Twitter mining showed that 56% of Indonesian had negative tweet in January 2021 (Pristiyono et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

COVID-19 vaccines knowledge and acceptance among Indonesian adults in Java Island

et al. 2023

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Background: To increase vaccination coverage, it is important to understand COVID-19 vaccination programs and respondents’ acceptance. Therefore, this study aimed to measure respondents’ knowledge of the COVID-19 vaccine and its acceptance among Indonesian adults in Java. Methods: A web-based survey was distributed through social media on self-claimed knowledge, risk and benefits of the vaccine, as well as respondents’ acceptance and experiences of the vaccination. The survey period was from March to July 2021, and 910 responses were included for further analysis. The frequency of each categorical factor, including self-claimed knowledge of the COVID-19 vaccine, their descriptive benefit and side effects of the COVID-19 vaccine, and their experiences receiving or not receiving the vaccine were explored. Predictor factors on vaccine knowledge and acceptance are investigated using multivariate ordinal regression analysis. Results: This study showed that almost all the respondents in both groups have knowledge about COVID-19 vaccination, or at least ever heard about it. The main source of information is social media. More than two third of respondents from each group had already received a COVID-19 vaccine or were at least on the waiting list. Moreover, a quarter of the respondents still hesitate to receive the vaccination. Only less than 10% of respondents reject the vaccination, with the strongest reason being scared of the side effect. Moreover, it found that respondents’ knowledge of the vaccination was influenced by age, medical background, a history of relatives who tested positive for COVID-19, source of information, economic status, and education levels. Moreover, the acceptance was influenced by age, knowledge about vaccines, and having medical background. Conclusions: This study showed high levels of knowledge and acceptance of the COVID-19 vaccine among adults in Java. Increasing understanding or knowledge about COVID-19 vaccine risks and benefits is necessary to reduce vaccination hesitancy.

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Section: Discussionmentioning

confidence: 99%

COVID-19 vaccines knowledge and acceptance among Indonesian adults in Java Island

et al. 2023

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“…Their findings underscored the substantial impact of investor actions on contagion complexity, elucidating diverse effects across market conditions, development levels, regions, and contagion directions. Xu et al (2022) delved into the multifaceted sentiments surrounding COVID-19 vaccines on Twitter, utilizing the VADER model. Their study revealed contrasting attitudes between China and other nations, shaped by fluctuating case numbers and public concerns.…”

Section: Yuan Et Al (2022) Conducted An Extensive Analysis Of Covid-1...mentioning

confidence: 99%

Unveiling COVID-19’s impact on Financial Stability: A Comprehensive Study of Price Dynamics and Investor Behavior in G7 Markets

Talbi,

Ferchichi,

Ismaalia

et al. 2024

IJEFI

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The paper delves into the impact of the COVID-19 pandemic on foreign stock markets across several developed nations. It seeks to empirically validate the presence of contagion by employing an adjusted correlation test spanning 7 developing stock markets from February 1, 1992, to April 31, 2021. Employing the FIEGARCH (1.1), DCC-MGARCH(1,1), and Switching-Markov analysis models, the research uncovers compelling evidence of the pandemic's influence on most developed countries during the COVID-19 period. Notably, these markets appear significantly susceptible to the adverse effects brought about by the pandemic. Recognizing the substantial ramifications of financial downturns on monetary policy, risk assessment, asset valuation, and portfolio distribution, the findings hold paramount significance for policymakers, investors, and portfolio managers. This empirical investigation offers insights that could profoundly impact decision-making strategies in these domains, shedding light on crucial aspects for informed policy adjustments, investment decisions, and portfolio allocations amidst such critical market fluctuations.

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“…individuals frequently express their views, opinions and emotions about the events of the pandemic on Twitter, Facebook, etc. (Xu et al, 2022), and create the need to manage and monitor these data in order to obtain relevant information (Piccarozzi & Aquilani, 2022) as well as to manage the changing needs of many economic sectors. Moreover, for COVID-19 pandemic no past observations could be used to provide a relevant signal about its potential economic (Barbaglia et al, 2022), and not only, impact.…”

Section: Big Data and Covid-19 Related Analysismentioning

confidence: 99%

“…Lately, several studies have been published that explore COVID-19 vaccination hesitancy using sentiment analysis and data from social networks. For such cases the interested reader is referred to (Xu et al, 2022) and Qorib et al (2022). In the latter publication, apart from the proposed methodology, a comparative study of 14 publications on COVID-19 vaccine Sentiment Analysis is presented.…”

Section: Big Data and Covid-19 Related Analysismentioning

confidence: 99%

Big Data, Sentiment Analysis, and Examples during the COVID-19 Pandemic

2022

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Applied research in Big Data has gained popularity and is already transforming corporations, public sector, health care and subsequently everyday life. Big Data are being analysed for a variety of reasons, e.g., predict Brexit negotiating outcomes, optimise operations in agriculture, map and analyse human mobility trends under non-pharmaceutical interventions during the recent pandemic. The period of the COVID-19 pandemic has been characterised also by an ‘infodemic’, meaning an overabundance of both good and bad information. This information needs to be managed effectively as it can yield valuable insights when analysed. In this paper the terms of Big Data, Geospatial Big Data and Sentiment Analysis are presented along with selected cases, from the international literature, of the use of Big Data and analytics during the COVID-19 pandemic.

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COVID-19 vaccine sensing: Sentiment analysis and subject distillation from twitter data

Cited by 12 publications

References 33 publications

COVID-19 vaccines knowledge and acceptance among Indonesian adults in Java Island

COVID-19 vaccines knowledge and acceptance among Indonesian adults in Java Island

Unveiling COVID-19’s impact on Financial Stability: A Comprehensive Study of Price Dynamics and Investor Behavior in G7 Markets

Big Data, Sentiment Analysis, and Examples during the COVID-19 Pandemic

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