Zamani Mohammadzaman, Schwartz H Andrew, Eichstaedt Johannes, Guntuku Sharath Chandra, Ganesan Adithya Virinchipuram, Clouston Sean, Giorgi Salvatore
Stony Brook University.
Stanford University.
Proc Conf Empir Methods Nat Lang Process. 2020 Nov;2020:193-198. doi: 10.18653/v1/2020.nlpcss-1.21.
The novelty and global scale of the COVID-19 pandemic has lead to rapid societal changes in a short span of time. As government policy and health measures shift, public perceptions and concerns also change, an evolution documented within discourse on social media. We propose a dynamic content-specific LDA topic modeling technique that can help to identify different domains of COVID-specific discourse that can be used to track societal shifts in concerns or views. Our experiments show that these model-derived topics are more coherent than standard LDA topics, and also provide new features that are more helpful in prediction of COVID-19 related outcomes including mobility and unemployment rate.
新冠疫情的新颖性和全球规模在短时间内导致了社会的迅速变化。随着政府政策和卫生措施的转变，公众的认知和担忧也在改变，社交媒体话语中记录了这一演变过程。我们提出了一种动态的特定内容LDA主题建模技术，该技术有助于识别新冠特定话语的不同领域，可用于追踪关注点或观点的社会转变。我们的实验表明，这些模型衍生的主题比标准LDA主题更连贯，还提供了更有助于预测与新冠疫情相关结果（包括流动性和失业率）的新特征。
