COVID-19 Vaccine Hesitancy in the Month Following the Start of the Vaccination Process

Cotfas, Liviu‐Adrian; Delcea, Camelia; Gherai, Rareș

doi:10.3390/ijerph181910438

Cited by 35 publications

(65 citation statements)

References 46 publications

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“…As in many other aspects of our lives, social media has become one of the channels in which individuals from all over the world have met and exposed ideas related to personal life choices or have criticized/supported the policies taken by the governments [13][14][15]. Even in the case of mask-wearing, social media have offered the possibility to people located in different parts of the world, some of them in partial or total isolation, to virtually meet and exchange opinions related to mask-wearing in the context of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

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Unmasking People’s Opinions behind Mask-Wearing during COVID-19 Pandemic—A Twitter Stance Analysis

et al. 2021

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Wearing a mask by the general public has been a controversial issue from the beginning of the COVID-19 pandemic as the public authorities have had mixed messages, either advising people not to wear masks if uninfected, to wear as a protective measure, to wear them only when inside a building/room with insufficient air flow or to wear them in all the public places. To date, the governments have had different policies regarding mask-wearing by the general public depending on the COVID-19 pandemic evolution. In this context, the paper analyzes the general public’s opinion regarding mask-wearing for the one-year period starting from 9 January 2020, when the first tweet regarding mask-wearing in the COVID-19 context has been posted. Classical machine learning and deep learning algorithms have been considered in analyzing the 8,795,633 tweets extracted. A random sample of 29,613 tweets has been extracted and annotated. The tweets containing news and information related to mask-wearing have been included in the neutral category, while the ones containing people’s opinions (for or against) have been marked using a symmetrical approach into in favor and against categories. Based on the analysis, it has been determined that most of the mask tweets are in the area of in favor or neutral, while a smaller percentage of tweets and retweets are in the against category. The evolution of the opinions expressed through tweets can be further monitored for extracting the public perspective on mask-wearing in times of COVID-19.

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Section: Introductionmentioning

confidence: 99%

“…Knowing that some of the classification algorithms rely on the word frequency, the Term Frequency-nverse Document Frequency (TF-IDF) has been used for reducing the weight of the most frequent words, which, in general, contain little to no information [15].…”

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confidence: 99%

Unmasking People’s Opinions behind Mask-Wearing during COVID-19 Pandemic—A Twitter Stance Analysis

et al. 2021

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“…The authors studied the period between November 2020 and February 2021 and determined that the negative sentiment declined over time [35]. Similar periods of time were considered by Liu and Liu [36] and Cotfas et al [13,14] regarding English tweets. Liu and Liu [36] showed that the public sentiment on COVID-19 vaccines varied over time and depended on geographic location, and Cotfas et al [13,14] underlined that the general public interest in the vaccination process has presented spikes that can be connected with the major events presented in the media in the selected period.…”

Section: Literature Reviewmentioning

confidence: 85%

“…Regarding the general opinion related to the COVID-19 vaccine expressed on Twitter, the discussions have evolved over time from simple messages, such as "I refuse", "I don't", "Not in my veins" and "Please vaccinate" to more complicated and better-defended arguments for and against vaccination, some of them mentioning and quoting the opinions of well-known physicians and researchers [13,14]. As a result, the analysis of the messages posted on Twitter can bring new insights into the reasons behind people's decisions to take the COVID-19 vaccine, either the 1st, 2nd or 3rd dose.…”

Section: Introductionmentioning

confidence: 99%

New Wave of COVID-19 Vaccine Opinions in the Month the 3rd Booster Dose Arrived

et al. 2022

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Vaccination has been proposed as one of the most effective methods to combat the COVID-19 pandemic. Since the day the first vaccine, with an efficiency of more than 90%, was announced, the entire vaccination process and its possible consequences in large populations have generated a series of discussions on social media. Whereas the opinions triggered by the administration of the initial COVID-19 vaccine doses have been discussed in depth in the scientific literature, the approval of the so-called 3rd booster dose has only been analyzed in country-specific studies, primarily using questionnaires. In this context, the present paper conducts a stance analysis using a transformer-based deep learning model on a dataset containing 3,841,594 tweets in English collected between 12 July 2021 and 11 August 2021 (the month in which the 3rd dose arrived) and compares the opinions (in favor, neutral and against) with the ones extracted at the beginning of the vaccination process. In terms of COVID-19 vaccination hesitance, an analysis based on hashtags, n-grams and latent Dirichlet allocation is performed that highlights the main reasons behind the reluctance to vaccinate. The proposed approach can be useful in the context of the campaigns related to COVID-19 vaccination as it provides insights related to the public opinion and can be useful in creating communication messages to support the vaccination campaign.

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“…Analyses of vaccine-related content available on social media platforms have been conducted in the world. Those analyses are based on data extracted from social media platforms and examine vaccine hesitancy, acceptance, sentiment and administrative burden [ 24 , 25 , 26 , 27 , 28 ]. Study data acquisition from Facebook and Twitter commonly relies on automated IT solutions offering data collection, monitoring and analysis [ 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

HPV Vaccination: Polish-Language Facebook Discourse Analysis

Sobeczek

Gujski

Raciborski

2022

IJERPH

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Social media platforms are widely used for spreading vaccine-related information. The objectives of this paper are to characterize Polish-language human papillomavirus (HPV) vaccination discourse on Facebook and to trace the possible influence of the COVID-19 pandemic on changes in the HPV vaccination debate. A quantitative and qualitative analysis was carried out based on data collected with a tool for internet monitoring and social media analysis. We found that the discourse about HPV vaccination bearing negative sentiment is centralized. There are leaders whose posts generate the bulk of anti-vaccine traffic and who possess relatively greater capability to influence recipients’ opinions. At the beginning of the COVID-19 pandemic vaccination debate intensified, but there is no unequivocal evidence to suggest that interest in the HPV vaccination topic changed.

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COVID-19 Vaccine Hesitancy in the Month Following the Start of the Vaccination Process

Cited by 35 publications

References 46 publications

Unmasking People’s Opinions behind Mask-Wearing during COVID-19 Pandemic—A Twitter Stance Analysis

Unmasking People’s Opinions behind Mask-Wearing during COVID-19 Pandemic—A Twitter Stance Analysis

New Wave of COVID-19 Vaccine Opinions in the Month the 3rd Booster Dose Arrived

HPV Vaccination: Polish-Language Facebook Discourse Analysis

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