Administering COVID-19 vaccines at a societal scale has been deemed as the most appropriate way to defend against the COVID-19 pandemic. This global vaccination drive naturally fueled a possibility of Pro-Vaxxers and Anti-Vaxxers strongly expressing their supports and concerns regarding the vaccines on social media platforms. Understanding this online discourse is crucial for policy makers. This understanding is likely to impact the success of vaccination drives and might even impact the final outcome of our fight against the pandemic. The goal of this work is to improve this understanding using the lens of Twitter-discourse data. We first develop a classifier that categorizes users according to their vaccine-related stance with high precision (97%). Using this method we detect and investigate specific user-groups who posted about vaccines in pre-COVID and COVID times. Specifically, we identify distinct topics that these users talk about, and investigate how vaccine-related discourse has changed between pre-COVID times and COVID times. Finally, for the first time, we investigate the change of vaccine-related stances in Twitter users and shed light on potential reasons for such changes in stance. Our dataset and classifier are available at https://github.com/sohampoddar26/covid-vax-stance.
翻译:以社会规模管理COVID-19疫苗被认为是防止COVID-19大流行病的最适当方法。这一全球疫苗接种运动自然地刺激了Pro-Vaxxers和Anti-Vaxxers在社交媒体平台上强烈表达对疫苗的支持和关切的可能性。理解这一在线讨论对于决策者至关重要。这种认识有可能影响疫苗接种运动的成功,甚至可能影响我们防治这一流行病斗争的最后结果。这项工作的目标是利用Twitter-Dision数据的镜头来提高这种认识。我们首先开发一个分类器,根据用户与疫苗有关的立场对用户进行高度精确的分类(97 % )。我们利用这个方法来探测和调查在COVID前和COVID时代张贴疫苗的具体用户群体。具体地说,我们查明这些用户谈论的不同主题,并调查与疫苗有关的讨论在COVID前的时间和COVID的时间之间是如何变化的。最后,我们首次调查了Twitter用户与疫苗有关的立场的变化,并揭示了这种立场变化的潜在原因。我们的数据设置和分类/分类系统在 https-babs/darbs/dalbs/darvar。