Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling
| Autor: | Adithya V Ganesan, H. Andrew Schwartz, Sharath Chandra Guntuku, Salvatore Giorgi, Sean A. P. Clouston, Johannes C. Eichstaedt, Mohammadzaman Zamani |
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| Rok vydání: | 2020 |
| Předmět: |
Topic model
Coronavirus disease 2019 (COVID-19) Computer science media_common.quotation_subject Novelty Public policy 030208 emergency & critical care medicine Dynamic web page Data science Article 03 medical and health sciences 0302 clinical medicine Perception Scale (social sciences) Social media 030212 general & internal medicine media_common |
| Zdroj: | Proc Conf Empir Methods Nat Lang Process |
| DOI: | 10.18653/v1/2020.nlpcss-1.21 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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