Workshop Proceedings of the 17th International AAAI Conference on Web and Social Media

Workshop: Workshop on Data for the Wellbeing of Most Vulnerable

DOI: 10.36190/2023.07

Published: 2023-06-01
CoVaxNet: An Online-Offline Data Repository for COVID-19 Vaccine Research
Bohan Jiang, Paras Sheth, Baoxin Li, Huan Liu

Despite the remarkable success of COVID-19 vaccines in combatting the virus, a significant portion of the population remains hesitant to receive vaccination, undermining government efforts to control the virus. To address this issue, it is essential to comprehend the various factors contributing to such behavior, including social media discourse, news media propaganda, government responses, demographic and socioeconomic factors, and COVID-19 statistics, among others. However, existing datasets do not encompass all these aspects, hindering the formation of a comprehensive understanding of vaccine hesitancy. In this paper, we develop a multi-source, multi-modal, and multi-feature online-offline data repository, CoVaxNet. We offer descriptive analyses and insights to illustrate critical patterns within CoVaxNet. Furthermore, we introduce a novel approach for connecting online and offline data to facilitate inference tasks that utilize complementary information sources.