Abstract

     To eliminate the impact of contradictory information on vaccine hesitancy on social media, this research developed a framework to compare the popularity of information expressing contradictory attitudes towards COVID-19 vaccine or vaccination, mine the similarities and differences among contradictory information’s characteristics, and determine which factors influenced the popularity mostly. We called Sina Weibo API to collect data. Firstly, to extract multi-dimensional features from original tweets and quantify their popularity, content analysis, sentiment computing and k-medoids clustering were used. Statistical analysis showed that anti-vaccine tweets were more popular than pro-vaccine tweets, but not significant. Then, by visualizing the features’ centrality and clustering in information-feature networks, we found that there were differences in text characteristics, information display dimension, topic, sentiment, readability, posters’ characteristics of the original tweets expressing different attitudes. Finally, we employed regression models and SHapley Additive exPlanations to explore and explain the relationship between tweets’ popularity and content and contextual features. Suggestions for adjusting the organizational strategy of contradictory information to control its popularity from different dimensions, such as poster’s influence, activity and identity, tweets’ topic, sentiment, readability were proposed, to reduce vaccine hesitancy.

Introduction

     In China, as of April 8, 2020, the number of confirmed cases of COVID-19 reached approximately 80,000. Although physical preventive measures such as wearing masks and social distancing effectively cut off the spread of the virus, long-term control of the COVID-19 pandemic hinged on the development and uptake of vaccines (Chou & Budenz, 2020). In March 2020, an anonymous cross-sectional survey, conducted online among Chinese adults, showed that 91.3% of participants would accept COVID-19 vaccination after the vaccine became available, among whom 52.2% wanted to get vaccinated as soon as possible, while others would delay vaccination until the vaccine’ safety was confirmed (J. Wang, Jing, et al., 2020). As a preventive innovation, vaccines’ diffusion and adoption are inevitably influenced by the competing dissemination of contradictory information expressing different attitudes towards vaccine and vaccination on social media (Cohen & Head, 2013; Pan & Di Zhang, 2020). Social media such as: Twitter (Jamison et al., 2020), Facebook (Xu & Guo, 2018), Instagram (Massey et al., 2020), YouTube (Ekram et al., 2019) etc., is not only an important resource for obtaining health information, but also serves as a breeding ground of health misinformation (Y. Wang, McKee, et al., 2019).

Conclusions

     This research firstly evaluated and compared the popularity of information expressing different attitudes towards COVID-19 vaccine or vaccination to reflect the vaccine hesitancy on social media. Then, it extracted the content and contextual features, visualized and compared their combining patterns frequently used in different-attitude information. Finally, it clarified the direction and degree of impact of features on information popularity. These findings could provide several suggestions for adjusting organizational strategies of contradictory information to reduce vaccine hesitancy.