Kang Gloria J, Ewing-Nelson Sinclair R, Mackey Lauren, Schlitt James T, Marathe Achla, Abbas Kaja M, Swarup Samarth
Department of Population Health Sciences, Virginia Tech, USA; Biocomplexity Institute, Virginia Tech, USA.
Biocomplexity Institute, Virginia Tech, USA.
Vaccine. 2017 Jun 22;35(29):3621-3638. doi: 10.1016/j.vaccine.2017.05.052. Epub 2017 May 27.
To examine current vaccine sentiment on social media by constructing and analyzing semantic networks of vaccine information from highly shared websites of Twitter users in the United States; and to assist public health communication of vaccines.
Vaccine hesitancy continues to contribute to suboptimal vaccination coverage in the United States, posing significant risk of disease outbreaks, yet remains poorly understood.
We constructed semantic networks of vaccine information from internet articles shared by Twitter users in the United States. We analyzed resulting network topology, compared semantic differences, and identified the most salient concepts within networks expressing positive, negative, and neutral vaccine sentiment.
The semantic network of positive vaccine sentiment demonstrated greater cohesiveness in discourse compared to the larger, less-connected network of negative vaccine sentiment. The positive sentiment network centered around parents and focused on communicating health risks and benefits, highlighting medical concepts such as measles, autism, HPV vaccine, vaccine-autism link, meningococcal disease, and MMR vaccine. In contrast, the negative network centered around children and focused on organizational bodies such as CDC, vaccine industry, doctors, mainstream media, pharmaceutical companies, and United States. The prevalence of negative vaccine sentiment was demonstrated through diverse messaging, framed around skepticism and distrust of government organizations that communicate scientific evidence supporting positive vaccine benefits.
Semantic network analysis of vaccine sentiment in online social media can enhance understanding of the scope and variability of current attitudes and beliefs toward vaccines. Our study synthesizes quantitative and qualitative evidence from an interdisciplinary approach to better understand complex drivers of vaccine hesitancy for public health communication, to improve vaccine confidence and vaccination coverage in the United States.
通过构建和分析来自美国推特用户高度分享网站的疫苗信息语义网络，研究社交媒体上当前的疫苗情绪；并协助开展疫苗的公共卫生宣传。
疫苗犹豫继续导致美国疫苗接种覆盖率不理想，带来疾病爆发的重大风险，但人们对此仍知之甚少。
我们构建了美国推特用户分享的互联网文章的疫苗信息语义网络。我们分析了由此产生的网络拓扑结构，比较了语义差异，并确定了在表达积极、消极和中性疫苗情绪的网络中最突出的概念。
与更大、联系较少的消极疫苗情绪网络相比，积极疫苗情绪的语义网络在话语中表现出更大的凝聚力。积极情绪网络以父母为中心，专注于传达健康风险和益处，突出了麻疹、自闭症、人乳头瘤病毒疫苗、疫苗与自闭症的联系、脑膜炎球菌病和麻疹-腮腺炎-风疹疫苗等医学概念。相比之下，消极网络以儿童为中心，专注于疾病控制与预防中心、疫苗行业、医生、主流媒体、制药公司和美国等组织团体。消极疫苗情绪的普遍存在通过多样化的信息得以体现，这些信息围绕着对传达支持疫苗积极益处的科学证据的政府组织的怀疑和不信任展开。
对在线社交媒体中疫苗情绪的语义网络分析可以增进对当前对疫苗态度和信念的范围及变异性的理解。我们的研究综合了跨学科方法的定量和定性证据，以更好地理解疫苗犹豫的复杂驱动因素，用于公共卫生宣传，提高美国的疫苗信心和疫苗接种覆盖率。
