Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling.
Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling.
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DOI：
10.18653/v1/2020.nlpcss-1.21
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Giorgi S
中科院分区：
文献类型：
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作者：
Zamani M;Schwartz HA;Eichstaedt J;Guntuku SC;Ganesan AV;Clouston S;Giorgi S
COVID-19大流行的新颖性和全球规模在短时间内导致了快速的社会变化。随着政府政策和卫生措施的转变，公众的看法和关切也发生了变化，这一演变在社交媒体上的话语中得到了记录。我们提出了一种动态内容特定的LDA主题建模技术，可以帮助识别特定于covid - 19的话语的不同领域，这些话语可用于跟踪关注或观点的社会转变。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，并且还提供了更有助于预测COVID-19相关结果（包括流动性和失业率）的新特征。
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.