ABSTRACT
Vaccine hesitancy is considered as one main cause of the stagnant uptake ratio of COVID-19 vaccines in Europe and the US where vaccines are sufficiently supplied. A fast and accurate grasp of public attitudes toward vaccination is critical to addressing vaccine hesitancy, and social media platforms have proved to be an effective source of public opinions. In this paper, we describe the collection and release of a dataset of tweets related to COVID-19 vaccines. This dataset consists of the IDs of 2,198,090 tweets collected from Western Europe, 17,934 of which are annotated with the originators' vaccination stances. Our annotation will facilitate using and developing data-driven models to extract vaccination attitudes from social media posts and thus further confirm the power of social media in public health surveillance. To lay the groundwork for future research, we not only perform statistical analysis and visualization of our dataset, but also evaluate and compare the performance of established text-based benchmarks in vaccination stance extraction. We demonstrate one potential use of our data in practice in tracking the temporal changes in public COVID-19 vaccination attitudes.
ABSTRACT
An information outbreak occurs on social media along with the COVID-19 pandemic and leads to an infodemic. Predicting the popularity of online content, known as cascade prediction, allows for not only catching in advance information that deserves attention, but also identifying false information that will widely spread and require quick response to mitigate its negative impact. Among the various information diffusion patterns leveraged in previous works, the spillover effect of the information exposed to users on their decisions to participate in diffusing certain information has not been studied. In this paper, we focus on the diffusion of information related to COVID-19 preventive measures due to its special role in consolidating public efforts to slow down the spread of the virus. Through our collected Twitter dataset, we validate the existence of the spillover effects. Building on this finding, we propose extensions to three cascade prediction methods based on Graph Neural Networks (GNNs). Experiments conducted on our dataset demonstrated that the use of the identified spillover effects significantly improves the state-of-the-art GNN methods in predicting the popularity of not only preventive measure messages, but also other COVID-19 messages.
ABSTRACT
As the impacts of the COVID-19 pandemic play out globally, the banking industry has been affected in both positive and negative ways, with the crisis creating both opportunities and threats for the collaborations between FinTech and banks. The aim of this study is to investigate the impact of FinTech products (FTPs) on commercial bank's performance in China. Required data are collected with a quantitative approach and two self-designed questionnaires were distributed to customers and employees of commercial banks in China. The gathered data were examined using the structural equation modeling technique. The results of this study reveal that the perceived usefulness (PU) of FTPs has positive and significant impacts on customer satisfaction, low expectation of bank employee assistance, bank's service quality and employee work efficiency. Additionally, the perceived difficulty of use (PD) of FTPs has negative and significant impacts on customer satisfaction and low expectation of assistance. Interestingly, there is a positive and significant relationship between PD and banks' service quality and work efficiency, meaning that the service quality and work efficiency can reduce some shortcomings of using FTPs. This study recognizes the need to enhance the understanding of FTPs on non-financial firm performance. This is the first study that helps commercial banks in China understand the perception of FTPs from both customer and employee perspectives.