Pro- and anti-vaccination arguments in the online vaccination discourse on Twitter
MetadataShow full item record
Background. Vaccinations help prevent the spread of diseases and save healthcare costs. Herd immunity exists at a high level of vaccine coverage and improves individual and community health and can be jeopardized by anti-vaccination movements. According to literature, anti-vaccination proponents generally base their arguments on beliefs and mistrust while provaccination proponents rely on science. Both pro- and antivaccination movements impact the online vaccination discourse and can influence vaccine-related decisions. Understanding the arguments used by Twitter users can combat the online antivaccination movement and increase willingness to vaccinate, resulting in herd immunity and improved health outcomes. Methodology. A vaccination decision-making framework is created to visualize various arguments used in vaccination debates. This framework is based on qualitative studies from literature and used as a coding scheme for vaccination tweets. Prior to this research, a selection of 2,000 tweets was gleaned from database containing 85,000 tweets. These selected tweets were coded manually in Excel by six coders in various categories including pro-vaccination, anti-vaccination and hesitant. Any doubts were discussed among the coders. Then, these pro-, anti- and hesitant vaccination tweets were coded top-down on content, substantiated by bottom-up codes. Solely the pro- and anti-vaccination tweets were coded in NVivo based on the aforementioned vaccination decision-making framework. Finally, in data analysis the pro- and anti-vaccination arguments were compared to each other. Results. In pro-vaccination, the most mentioned themes were preventive health beliefs, risk, health freedom, media, reliability, vaccine effectiveness and social experiences. In anti- vaccination, the most mentioned themes were vaccine safety, trust in government and social experiences As expected, provaccination arguments are more based on science while antivaccination arguments are more focused on beliefs and mistrust. Several themes were unmentioned, implying that Twitter users value them less than expected in literature studies. Discussion. Differences in outcomes between this research and the literature can be explained by country, tweet selection, the time period of the tweets and study population. Strengths of this research include the representation of reality, inter-coder reliability in Excel, an interdisciplinary approach and the jointly analysis of pro- and anti-vaccination tweets. Limitations of this research include the lack of inter-coder reliability in NVivo, little variation and validity in the (amount of) tweets, the difficulty of interpretation of tweets and the fact that the current COVID-19 pandemic might have changed the attention, willingness and hesitancy towards vaccines. Further research would include a more explorative analysis on the influence of Twitter on vaccine decision-making. Conclusion. In line with the literature, the arguments in provaccination tweets are majorly based on science and the antivaccination tweets are more based on beliefs, attitudes and mistrust.