CoVaxNet: An Online-Offline Data Repository for COVID-19 Vaccine Hesitancy Research
This work addresses the challenge of incomplete data for researchers studying vaccine hesitancy, which is incremental as it builds on existing datasets by integrating more sources.
The authors tackled the problem of understanding COVID-19 vaccine hesitancy by constructing CoVaxNet, a multi-source, multi-modal, and multi-feature online-offline data repository, and proposed a novel approach to connect online and offline data for improved inference tasks.
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.