论文标题
通过在线和离线数据相结合来改善疫苗立场检测
Improving Vaccine Stance Detection by Combining Online and Offline Data
论文作者
论文摘要
关于Covid-19的不同意见导致了有关疫苗的各种在线话语。由于有害的影响和COVID-19大流行的规模，检测疫苗的立场变得尤为重要，并且正在引起越来越多的关注。大流行期间的交流通常是通过在线和离线资源进行的，这为检测疫苗姿态提供了两种互补的途径。因此，本文旨在（1）研究将在线数据和离线数据集成到疫苗姿态检测的重要性； （2）确定影响个人疫苗立场的关键在线和离线属性。我们将疫苗犹豫不决模型为确定在线和离线因素的重要性的替代物。在可解释的AI和组合分析的帮助下，我们得出结论，在线和离线因素都有助于预测疫苗的立场。
Differing opinions about COVID-19 have led to various online discourses regarding vaccines. Due to the detrimental effects and the scale of the COVID-19 pandemic, detecting vaccine stance has become especially important and is attracting increasing attention. Communication during the pandemic is typically done via online and offline sources, which provide two complementary avenues for detecting vaccine stance. Therefore, this paper aims to (1) study the importance of integrating online and offline data to vaccine stance detection; and (2) identify the critical online and offline attributes that influence an individual's vaccine stance. We model vaccine hesitancy as a surrogate for identifying the importance of online and offline factors. With the aid of explainable AI and combinatorial analysis, we conclude that both online and offline factors help predict vaccine stance.
