Adithya Virinchipuram Ganesan


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Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling
Mohammadzaman Zamani | H. Andrew Schwartz | Johannes Eichstaedt | Sharath Chandra Guntuku | Adithya Virinchipuram Ganesan | Sean Clouston | Salvatore Giorgi
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

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 social mobility and unemployment rate.