Xie Lei, Wang Dandan, Ma Feicheng
School of Information Management, Wuhan University, Wuhan, 430072, China.
Center for Studies of Information Resources, Wuhan University, Wuhan, 430072, China.
Comput Human Behav. 2023 Jun;143:107649. doi: 10.1016/j.chb.2022.107649. Epub 2023 Jan 17.
During the COVID-19 pandemic, vaccine hesitancy proved to be a major obstacle in efforts to control and mitigate the negative consequences of COVID-19. This study centered on the degree of polarization on social media about vaccine use and contributing factors to vaccine hesitancy among social media users. Examining the discussion about COVID-19 vaccine on the Weibo platform, a relatively comprehensive system of user features was constructed based on psychological theories and models such as the curiosity-drive theory and the big five model of personality. Then machine learning methods were used to explore the paramount impacting factors that led users into polarization. Findings revealed that factors reflecting the activity and effectiveness of social media use promoted user polarization. In contrast, features reflecting users' information processing ability and personal qualities had a negative impact on polarization. This study hopes to help healthcare organizations and governments understand and curb social media polarization around vaccine development in the face of future surges of pandemics.
在新冠疫情期间,疫苗犹豫被证明是控制和减轻新冠疫情负面影响的努力中的一个主要障碍。本研究聚焦于社交媒体上关于疫苗使用的两极分化程度以及社交媒体用户中导致疫苗犹豫的影响因素。通过研究微博平台上关于新冠疫苗的讨论,基于好奇心驱动理论和大五人格模型等心理学理论和模型构建了一个相对全面的用户特征系统。然后使用机器学习方法来探索导致用户两极分化的首要影响因素。研究结果表明,反映社交媒体使用活跃度和有效性的因素促进了用户两极分化。相比之下,反映用户信息处理能力和个人品质的特征对两极分化有负面影响。本研究希望帮助医疗保健组织和政府在未来面对疫情激增时,理解并遏制围绕疫苗研发的社交媒体两极分化现象。