Despite the astonishing success of COVID-19 vaccines against the virus, a substantial proportion of the population is still hesitant to be vaccinated, undermining governmental efforts to control the virus. To address this problem, we need to understand the different factors giving rise to such a behavior, including social media discourses, news media propaganda, government responses, demographic and socioeconomic statuses, and COVID-19 statistics, etc. However, existing datasets fail to cover all these aspects, making it difficult to form a complete picture in inferencing about the problem of vaccine hesitancy. In this paper, we construct a multi-source, multi-modal, and multi-feature online-offline data repository CoVaxNet. We provide descriptive analyses and insights to illustrate critical patterns in CoVaxNet. Moreover, we propose a novel approach for connecting online and offline data so as to facilitate the inference tasks that exploit complementary information sources.
翻译：尽管防治病毒的COVID-19疫苗取得了惊人的成功,但相当大比例的人口仍不愿接种疫苗,这破坏了政府控制病毒的努力。为了解决这一问题,我们需要理解导致这种行为的各种因素,包括社交媒体言论、新闻媒体宣传、政府反应、人口和社会经济状况以及COVID-19统计数据等。 然而,现有的数据集未能涵盖所有这些方面,因此很难形成一个完整的画面来推断疫苗不耐用的问题。在本文中,我们建立了一个多源、多模式和多功能在线犯罪数据储存库CoVaxNet。我们提供了描述性分析和洞察,以说明CoVaxNet的关键模式。此外,我们提出了将在线和离线数据连接起来的新办法,以便利利用补充信息来源的推论任务。
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