Impact of social media news on COVID-19 vaccine hesitancy and vaccination behavior

Zhang, Qianyao; Zhang, Runtong; Wu, Wen Chi; Liu, Yang; Yu, Zhou

doi:10.1016/j.tele.2023.101983

Cited by 14 publications

(2 citation statements)

References 112 publications

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“…While previous studies have used NLP for sentiment analysis on COVID-19 vaccination and information exposure analysis regarding the HPV vaccine using Twitter datasets [40,89], and have investigated the temporal and geographic variations in public perceptions of the HPV vaccine [90], our tool extends its functionality to include a broader spectrum of platforms for tracking different vaccine sentiment and hesitancy on social media. Despite the scientific evidence supporting the safety and efficacy of vaccines, vaccine hesitancy sentiments on social media can impact public confidence regarding vaccination [91]. Our tool is designed to quickly identify surges in vaccine hesitancy, thereby could be a tool to assist public health professionals in responding promptly with accurate information and effective vaccine promotion strategies.…”

Section: Advantages and Limitationsmentioning

confidence: 99%

Natural Language Processing–Powered Real-Time Monitoring Solution for Vaccine Sentiments and Hesitancy on Social Media: System Development and Validation (Preprint)

Huang,

Eiden,

et al. 2024

Preprint

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BACKGROUND Vaccines serve as a crucial public health tool, though vaccine hesitancy continues to pose a significant threat to full vaccine uptake and, consequently, community health. Understanding and tracking vaccine hesitancy is essential for effective public health interventions; however, traditional survey methods present various limitations. OBJECTIVE To create a real-time, natural language processing (NLP)-based tool to assess vaccine sentiment and hesitancy across three prominent social media platforms. METHODS We mined and curated discussions from Twitter, Reddit, and YouTube social media platforms, posted between January 1, 2011, and October 31, 2021, concerning human papillomavirus (HPV), measles, mumps, and rubella (MMR), and unspecified vaccines. We tested multiple NLP algorithms to classify vaccine sentiment into positive, neutral, or negative, and to classify vaccine hesitancy using the World Health Organization’s (WHO) 3Cs hesitancy model, conceptualizing an interactive dashboard to illustrate and contextualize trends. RESULTS We compiled over 86 million discussions. Our top-performing NLP models displayed accuracies ranging from 0.51 to 0.78 for sentiment classification and 0.69 to 0.91 for hesitancy classification. Explorative analysis on our platform highlighted variations in online activity about vaccine sentiment and hesitancy, suggesting unique patterns for different vaccines. CONCLUSIONS Our innovative system performs real-time analysis of sentiment and hesitancy on three vaccine topics across major social networks, providing crucial trend insights to assist campaigns aimed at enhancing vaccine uptake and public health.

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Section: Advantages and Limitationsmentioning

confidence: 99%

Natural Language Processing–Powered Real-Time Monitoring Solution for Vaccine Sentiments and Hesitancy on Social Media: System Development and Validation (Preprint)

Huang,

Eiden,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…A recent study on the impact of social media news on COVID-19 vaccine hesitancy and vaccination behavior suggests that individuals are more sensitive to vaccine risk news than safety news on social media, indicating a relationship between the type of information and its impact on perception (Zhang et al, 2023). This resonates with the ndings of this study, highlighting the complex nature of public sentiment, shaped by the interaction of trust, engagement, and the speci c content of vaccine-related information on social media.…”

Section: Shaping Targeted Strategiesmentioning

confidence: 99%

Social Media Influence on COVID-19 Vaccine Perceptions Among University Students: A Malawi Case Study

Folotiya,

Ngoma

2023

Preprint

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Introduction The global fight against the COVID-19 pandemic relies significantly on vaccination. The collective international effort has been massive, but the pace of vaccination finds hindrance due to supply and vaccine hesitancy factors. Understanding public perceptions, especially through the lens of social media, is important. This study investigates the impact of social media on COVID-19 vaccine perceptions among university students in Malawi. Methodology The study utilized a quantitative methodology and employed a cross-sectional study design to explore the relationship between social media dynamics and COVID-19 vaccine perceptions among 382 randomly sampled students at MUBAS. Data, collected by use of a Likert-scale questionnaire, was analyzed using IBM SPSS 20 for descriptive statistics and Pearson correlation tests. Results and discussion The findings reveal crucial correlations. Specifically, trust in online vaccine information shows a positive correlation (r = 0.296, p < 0.01) with active engagement in social media discussions. Conversely, a negative correlation surfaces concerning individuals' reactions to vaccine availability in Malawi (r = -0.026, p > 0.05), necessitating an exploration of influencing factors. The demographic overview underscores the need for tailored communication strategies, given the dominance of the 16 to 30 age group, representing 92.9% of respondents. Conclusion The identified correlations emphasize the need for careful communication strategies tailored to combat misinformation and enhance vaccine acceptance among the younger demographic in Malawi.

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Threshold dynamics of a stochastic infectious disease model with vaccination age under saturated media coverage

Yu,

Tan,

2024

J. Appl. Math. Comput.

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Impact of social media news on COVID-19 vaccine hesitancy and vaccination behavior

Cited by 14 publications

References 112 publications

Natural Language Processing–Powered Real-Time Monitoring Solution for Vaccine Sentiments and Hesitancy on Social Media: System Development and Validation (Preprint)

Natural Language Processing–Powered Real-Time Monitoring Solution for Vaccine Sentiments and Hesitancy on Social Media: System Development and Validation (Preprint)

Social Media Influence on COVID-19 Vaccine Perceptions Among University Students: A Malawi Case Study

Threshold dynamics of a stochastic infectious disease model with vaccination age under saturated media coverage

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