ABSTRACT

During the COVID-19 pandemic, social media platforms such as Twitter, Facebook, etc. have played an important role in conveying information, both accurate and inaccurate, thereby creating mass confusion. As the response to COVID-19 has reduced face-to-face contact, communication via social media has increased. Evidence shows that social media affects disease (non-)prevention through the (im)proper distribution of information, and distorts the predictive accuracy of infection models, including legacy Susceptible–Exposed–Infectious–Recovered (SEIR) models. Our adjusted SEIR model reflects the effectiveness of information disseminated through social media by accounting for dimensions of social/informational motivation based on social learning/use and gratification theories, and uses Monte Carlo methodology and computational algorithms to predict effects of social media on the spread of COVID-19 (N = 2,095 cases). The results suggest that social media utilisation measures should be incorporated into SEIR models to improve forecasts of COVID-19 infections. Utilising IS to analyse the spread of digital information via social media platforms can inform efforts to combat the pandemic and infodemic. Agencies responsible for infection and disease control, policy makers, businesses, institutions and educators must accurately monitor infection rates to appropriately allocate funding and human resources and develop effective disease prevention marketing campaigns.

摘要

在COVID-19大流行期间，Twitter，Facebook等社交媒体平台在传递准确和不准确的信息方面发挥了重要作用，从而造成了混乱。随着对COVID-19的回应减少了面对面的接触，通过社交媒体进行的交流也有所增加。有证据表明，社交媒体通过信息的（不正确）分布影响疾病（非）预防，并扭曲了感染模型的预测准确性，包括传统的易感－暴露－传染－恢复（SEIR）模型。我们调整后的SEIR模型通过考虑基于社会学习/使用和满足理论的社会/信息动机的维度，反映了通过社交媒体传播的信息的有效性，N = 2,095例）。结果表明，社交媒体利用措施应纳入SEIR模型中，以改善对COVID-19感染的预测。利用IS分析通过社交媒体平台传播的数字信息可以为抗击大流行和信息流行提供信息。负责感染和疾病控制的机构，决策者，企业，机构和教育工作者必须准确地监测感染率，以适当地分配资金和人力资源，并开展有效的疾病预防营销活动。