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Article
Health Care Sciences & Services
Luwen Huangfu et al.
Summary: This study utilized sentiment-based topic modeling to evaluate the public's awareness and attitude towards COVID-19 vaccines through Twitter data analysis. The study found that the main topics of positive attention towards COVID-19 vaccines were vaccination plans, vaccination behavior, and vaccine information and knowledge. On the other hand, the main topics of negative attention focused on vaccine hesitancy, extreme side effects of vaccines, and vaccine supply and rollout. The study also revealed that negative sentiment trends remained stable, while positive sentiments were easily influenced. These findings are important for policymakers and research communities to track public attitudes towards COVID-19 vaccines and make decisions to promote vaccination campaigns.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2022)
Article
Public, Environmental & Occupational Health
Young Anna Argyris et al.
Summary: The study compared the discursive topics chosen by pro- and antivaccine advocates in influencing the public, finding that antivaccine topics have greater intertopic distinctiveness and use all four message frames, while provaccine advocates have neglected having a clear problem statement.
JMIR PUBLIC HEALTH AND SURVEILLANCE
(2021)
Article
Infectious Diseases
Samira Yousefinaghani et al.
Summary: This study identified public sentiments and opinions towards COVID-19 vaccines on Twitter, showing a dominance of positive sentiments but active discussions on vaccine rejection and hesitancy. Different countries exhibited varying patterns. Additionally, the study found that vaccine opposition content came partly from Twitter bots or political activists, while support for vaccination originated from well-known individuals and organizations.
INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES
(2021)
Article
Computer Science, Information Systems
Diaa Salama Abdelminaam et al.
Summary: The COVID-19 pandemic has led to an increase in the spread of misinformation, highlighting the importance of computerized systems for detecting fake news. An updated deep neural network approach was proposed to distinguish between true and false information in a large dataset of tweets, showing high accuracy and significant improvement compared to traditional machine learning models.
Article
Communication
Mike Thelwall et al.
Summary: Vaccine hesitancy for Covid-19 may increase mortality rates and delay the easing of social distancing restrictions, largely due to online platforms spreading fear and misinformation. Content analysis of vaccine hesitant tweets on Twitter revealed themes of conspiracies, vaccine development speed, and safety, with a majority of refusers expressing right-wing opinions.
PROFESIONAL DE LA INFORMACION
(2021)
Proceedings Paper
Computer Science, Information Systems
Marco Furini et al.
3RD INTERNATIONAL WORKSHOP ON SOCIAL SENSING (SOCIALSENS 2018)
(2018)
Article
Immunology
Kate Faasse et al.
