Cotfas Liviu-Adrian, Crăciun Liliana, Delcea Camelia, Florescu Margareta Stela, Kovacs Erik-Robert, Molănescu Anca Gabriela, Orzan Mihai
Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010374 Bucharest, Romania.
Department of Economics and Economic Policies, Bucharest University of Economic Studies, 010374 Bucharest, Romania.
Vaccines (Basel). 2023 Aug 18;11(8):1381. doi: 10.3390/vaccines11081381.
Given the high amount of information available on social media, the paper explores the degree of vaccine hesitancy expressed in English tweets posted worldwide during two different one-month periods of time following the announcement regarding the discovery of new and highly contagious variants of COVID-19-Delta and Omicron. A total of 5,305,802 COVID-19 vaccine-related tweets have been extracted and analyzed using a transformer-based language model in order to detect tweets expressing vaccine hesitancy. The reasons behind vaccine hesitancy have been analyzed using a Latent Dirichlet Allocation approach. A comparison in terms of number of tweets and discussion topics is provided between the considered periods with the purpose of observing the differences both in quantity of tweets and the discussed discussion topics. Based on the extracted data, an increase in the proportion of hesitant tweets has been observed, from 4.31% during the period in which the Delta variant occurred to 11.22% in the Omicron case, accompanied by a diminishing in the number of reasons for not taking the vaccine, which calls into question the efficiency of the vaccination information campaigns. Considering the proposed approach, proper real-time monitoring can be conducted to better observe the evolution of the hesitant tweets and the COVID-19 vaccine hesitation reasons, allowing the decision-makers to conduct more appropriate information campaigns that better address the COVID-19 vaccine hesitancy.
鉴于社交媒体上存在大量信息，本文探讨了在宣布发现新冠病毒新的高传染性变种——德尔塔和奥密克戎之后的两个不同的一个月时间段内，全球发布的英文推文中所表达的疫苗犹豫程度。总共提取并分析了5305802条与新冠疫苗相关的推文，使用基于Transformer的语言模型来检测表达疫苗犹豫的推文。使用潜在狄利克雷分配方法分析了疫苗犹豫背后的原因。为了观察推文数量和讨论话题的差异，对所考虑的时间段之间的推文数量和讨论话题进行了比较。根据提取的数据，观察到犹豫推文的比例有所增加，从德尔塔变种出现期间的4.31%增至奥密克戎病例中的11.22%，同时不接种疫苗的原因数量有所减少，这让人对疫苗接种信息宣传活动的效率产生质疑。考虑到所提出的方法，可以进行适当的实时监测，以更好地观察犹豫推文的演变以及新冠疫苗犹豫的原因，使决策者能够开展更合适的信息宣传活动，更好地解决新冠疫苗犹豫问题。
