Trajectories of and spatial variations in HPV vaccine discussions on Weibo, 2018-2023: a deep learning analysis DOI Creative Commons
Wang You,

Haoyun Yang,

Zhijun Ding

и другие.

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Дек. 9, 2023

Summary Research in context Evidence before this study We first searched PubMed for articles published until November 2023 with the keywords “(“HPV”) AND (“Vaccine” or “Vaccination”) (“Social Media”)”. identified about 390 studies, most of which were discussions on potentials feasibility social media HPV vaccination advocacy research, manual coding-driven analyses text (eg., tweets) vaccines emerged platforms. When we added keyword “Machine Learning”, only 12 several them using AI-driven approach, such as deep learning, machine and natural language process, to analyze extensive data public perceptions perform monitor platforms, X (Twitter) Reddit. All these studies are from English-language platforms developed countries. No date has monitored developing countries including China. Added value This is deep-learning monitoring expressed Chinese (Weibo our case), revealing key temporal geographic variations. found a sustained high level positive attitude towards exposure norms facilitating among Weibo users, lower national prevalence negative attitude, perceived barriers accepting vaccination, misinformation indicating achievement relevant health communication. High practical was associated relatively insufficient vaccine accessibility China, suggesting systems should prioritize addressing issues supply. Lower perception male higher hesitancy 2-valent vaccine, provincial-level spatial cluster indicate that tailored strategies need be formed targeting specific population, areas, type. Our practice shows realizing surveillance potential listening context. Leveraging recent advances approach could cost-effective supplement existing techniques. Implications all available evidence highlights learning-driven convenient effective identifying emerging trends inform interventions. As techniques, it particularly helpful timely communication resource allocation at multiple levels. Key stakeholders officials maintain focus education highlighting risks consequences infections, benefits safety types vaccines; aim resolve accessibility. A proposed research area further development learning models analyzing Background rate low Understanding multidimensional impetuses by individuals essential. assess perceptions, barriers, facilitators platform Weibo. Methods collected posts regarding between 2018 2023. annotated 6,600 manually according behavior change theories, subsequently fine-tuned annotate collected. Based results models, conducted attitudes its determinants. Findings Totally 1,972,495 vaccines. Deep reached predictive accuracy 0.78 0.96 classifying posts. During 2023, 1,314,510 (66.6%) classified attitudes. And 224,130 (11.4%) misinformation, 328,442 (16.7%) vaccines, 580,590 (29.4%) vaccination. The increased 15.8% March 79.1% mid-2023 (p < 0.001), declined 36.6% mid-2018 10.7% (P .001). Central regions exhibited norms, whereas Shanghai, Beijing megacities northeastern showed misinformation. Positive significantly (65.7%), than 4-valent 9-valent (79.6% 74.1%). Interpretation Social represents promising can enable strategies.

Язык: Английский

Clarifying Misunderstandings in COVID-19 Vaccine Sentiment and Stance Analysis and Their Implications for Vaccine Hesitancy: A Systematic Review DOI Creative Commons
Lorena Barberia, Belinda Lombard, Norton Trevisan Roman

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Март 25, 2025

Abstract Background Advances in machine learning (ML) models have increased the capability of researchers to detect vaccine hesitancy social media using Natural Language Processing (NLP). A considerable volume research has identified persistence COVID-19 discourse shared on various platforms. Methods Our objective this study was conduct a systematic review employing sentiment analysis or stance detection towards vaccines and vaccination spread Twitter (officially known as X since 2023). Following registration PROSPERO international registry reviews, we searched papers published from 1 January 2020 31 December 2023 that used supervised assess through Twitter. We categorized studies according taxonomy five dimensions: tweet sample selection approach, self-reported type, classification typology, annotation codebook definitions, interpretation results. analyzed if report different trends than those by examining how is measured, whether efforts were made avoid measurement bias. Results found bias widely prevalent analyze toward vaccination. The reporting errors are sufficiently serious they hinder generalisability these understanding individual opinions communicate reluctance vaccinate against SARS-CoV-2. Conclusion Improving NLP methods crucial addressing knowledge gaps discourse.

Язык: Английский

Процитировано

0
Context matters: How to research vaccine attitudes and uptake after the COVID-19 crisis DOI Creative Commons
Jeremy K. Ward,

Patrick Peretti‐Watel,

Ève Dubé

и другие.

Human Vaccines & Immunotherapeutics, Год журнала: 2024, Номер 20(1)

Опубликована: Июнь 19, 2024

The pandemic dramatically accelerated research on vaccine attitudes and uptake, a field which mobilizes researchers from the social sciences humanities as well biomedical public health disciplines. has potential to contribute much more, but growth in deeper connections between disciplines brings challenges opportunities. This perspective article assesses recent development of field, exploring progress whilst emphasizing that not enough attention been paid national local contexts. lack contextual limits hinders our capacity learn COVID-19 crisis. We suggest three concrete responses: building recognizing new publishing formats for reporting synthesizing studies at country level; establishing country-level interdisciplinary networks connect praxis; strengthening international comparative survey work by enhancing focus factors.

Язык: Английский

Процитировано

3
Canadian health care providers' and education workers' hesitance to receive original and bivalent COVID-19 vaccines DOI Creative Commons
Brenda L. Coleman, Iris Gutmanis, Susan J. Bondy

и другие.

Vaccine, Год журнала: 2024, Номер 42(24), С. 126271 - 126271

Опубликована: Сен. 2, 2024

Background: The demand for COVID-19 vaccines has diminished as the pandemic lingers. Understanding vaccine hesitancy among essential workers is important in reducing impact of future pandemics by providing effective immunization programs delivered expeditiously. Method: Two surveys exploring acceptance 2021 and 2022 were conducted cohorts health care providers (HCP) education participating prospective studies illnesses uptake. Demographic factors opinions about (monovalent bivalent) public measures collected these self-reported surveys. Modified multivariable Poisson regression was used to determine associated with hesitancy. Results: In 2021, 3 % 2061 HCP 6 3417 reported (p < 0.001). December 2022, 21 868 24 1457 being hesitant receive a bivalent = 0.09). Hesitance be vaccinated monovalent earlier date survey completion, later receipt first dose, no influenza vaccination, less worry becoming ill COVID-19. Factors hesitance that common both two or fewer previous doses lower certainty safe effective. Conclusion: Education somewhat more likely than report but reasons similar. Hesitancy non-receipt (i.e., behaviour), concern infected SARS-CoV-2, concerns safety effectiveness cohorts. Maintaining inter-pandemic trust vaccines, ensuring rapid data generation during regarding effectiveness, transparent communication are all needed support vaccination programs.

Язык: Английский

Процитировано

3
Association Between Sociodemographic Factors and Vaccine Acceptance for Influenza and SARS-CoV-2 in South Korea: Nationwide Cross-Sectional Study DOI Creative Commons
Seohyun Hong, Yejun Son, Myeongcheol Lee

и другие.

JMIR Public Health and Surveillance, Год журнала: 2024, Номер 10, С. e56989 - e56989

Опубликована: Ноя. 1, 2024

Abstract Background The imperative arises to study the impact of socioeconomic factors on acceptance SARS-CoV-2 and influenza vaccines amid changes in immunization policies during COVID-19 pandemic. Objective To enhance targeted public health strategies improve age-specific based identified risk factors, this investigated associations between sociodemographic vaccination behaviors pandemic, with emphasis vaccine cost policies. Methods This analyzed data from Korean Community Health Survey 2019‐2022 507,964 participants investigate pandemic period. Cohorts aged 19‐64 years 65 or older were stratified age (years), indicators. cohorts assess influence relevant under by using weighted odds ratio (ROR). Results Among participants, (COVID-19 vaccine) was higher among individuals possibly indicating status, such as education level (age years: ROR 1.34; 95% CI 1.27‐1.40 ≥65 1.19; 1.01‐1.41) income 1.67; 1.58‐1.76 1.21; 1.06‐1.38) for both compared before In context cohort exhibited hesitancy associated care mobility lower general status (ROR 0.89; 0.81‐0.97). Conclusions should focus reducing social participation. younger participation, while efforts prioritize limited access services.

Язык: Английский

Процитировано

2
Web-Enhanced Vision Transformers and Deep Learning for Accurate Event-Centric Management Categorization in Education Institutions DOI Creative Commons
Khalied M. Albarrak, Shaymaa E. Sorour

Systems, Год журнала: 2024, Номер 12(11), С. 475 - 475

Опубликована: Ноя. 7, 2024

In the digital era, social media has become a cornerstone for educational institutions, driving public engagement and enhancing institutional communication. This study utilizes AI-driven image processing Web-enhanced Deep Learning (DL) techniques to investigate effectiveness of King Faisal University’s (KFU’s) strategy as case study, particularly on Twitter. By categorizing images into five primary event management categories subcategories, this research provides robust framework assessing content generated by KFU’s administrative units. Seven advanced models were developed, including an innovative integration Vision Transformers (ViTs) with Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, VGG16, ResNet. The ViT-CNN hybrid model achieved perfect classification accuracy (100%), while “Development Partnerships” category demonstrated notable (98.8%), underscoring model’s unparalleled efficacy in strategic classification. offers actionable insights optimization communication strategies data collection processes, aligning them national development goals Saudi Arabia’s 2030, thereby showcasing transformative power DL event-centric broader higher education landscape.

Язык: Английский

Процитировано

1
Transplacental transmission of mRNA injections confirmed: does this evidence by Hanna and colleagues support their proposition as a promising prenatal gene therapy? DOI Open Access
Siguna Mueller

Опубликована: Авг. 25, 2024

A recent study by Hanna and collaborators assessed the presence of COVID-19 mRNA productsin placenta umbilical cord blood following maternal “vaccination” during human pregnancy.Their analysis 2 pregnant women revealed that genetic injections was detected inboth placentas. Spike protein expression confirmed in one them. Furthermore,the engineered mother, wherethose samples were available for analysis. They also found integrity injected varied across samples, but overall, a substantial or even overwhelming majority consisted non-integrous species. The authors provide several theoretical reasons they believe explain their experiential observations. Based on results shifting aim, indicate benefits suggest related gene therapies, particularly mRNA-based treatments, may have great promise as prenatal therapy. This article analyses findings interpretations identifies gaps inconsistencies explanations. It provides short synopsis update known problems with outlines major pitfalls unknowns underlying thepromised intended therapy applications.

Язык: Английский

Процитировано

0
Trajectories of and spatial variations in HPV vaccine discussions on Weibo, 2018-2023: a deep learning analysis DOI Creative Commons
Wang You,

Haoyun Yang,

Zhijun Ding

и другие.

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Дек. 9, 2023

Summary Research in context Evidence before this study We first searched PubMed for articles published until November 2023 with the keywords “(“HPV”) AND (“Vaccine” or “Vaccination”) (“Social Media”)”. identified about 390 studies, most of which were discussions on potentials feasibility social media HPV vaccination advocacy research, manual coding-driven analyses text (eg., tweets) vaccines emerged platforms. When we added keyword “Machine Learning”, only 12 several them using AI-driven approach, such as deep learning, machine and natural language process, to analyze extensive data public perceptions perform monitor platforms, X (Twitter) Reddit. All these studies are from English-language platforms developed countries. No date has monitored developing countries including China. Added value This is deep-learning monitoring expressed Chinese (Weibo our case), revealing key temporal geographic variations. found a sustained high level positive attitude towards exposure norms facilitating among Weibo users, lower national prevalence negative attitude, perceived barriers accepting vaccination, misinformation indicating achievement relevant health communication. High practical was associated relatively insufficient vaccine accessibility China, suggesting systems should prioritize addressing issues supply. Lower perception male higher hesitancy 2-valent vaccine, provincial-level spatial cluster indicate that tailored strategies need be formed targeting specific population, areas, type. Our practice shows realizing surveillance potential listening context. Leveraging recent advances approach could cost-effective supplement existing techniques. Implications all available evidence highlights learning-driven convenient effective identifying emerging trends inform interventions. As techniques, it particularly helpful timely communication resource allocation at multiple levels. Key stakeholders officials maintain focus education highlighting risks consequences infections, benefits safety types vaccines; aim resolve accessibility. A proposed research area further development learning models analyzing Background rate low Understanding multidimensional impetuses by individuals essential. assess perceptions, barriers, facilitators platform Weibo. Methods collected posts regarding between 2018 2023. annotated 6,600 manually according behavior change theories, subsequently fine-tuned annotate collected. Based results models, conducted attitudes its determinants. Findings Totally 1,972,495 vaccines. Deep reached predictive accuracy 0.78 0.96 classifying posts. During 2023, 1,314,510 (66.6%) classified attitudes. And 224,130 (11.4%) misinformation, 328,442 (16.7%) vaccines, 580,590 (29.4%) vaccination. The increased 15.8% March 79.1% mid-2023 (p < 0.001), declined 36.6% mid-2018 10.7% (P .001). Central regions exhibited norms, whereas Shanghai, Beijing megacities northeastern showed misinformation. Positive significantly (65.7%), than 4-valent 9-valent (79.6% 74.1%). Interpretation Social represents promising can enable strategies.

Язык: Английский

Процитировано

0