{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/understanding-weekly-covid-19-concerns","title":"Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling","arxiv_id":null,"date":"2020-11-01","proceeding":"EMNLP (NLP+CSS) 2020 11","authors":["Mohammadzaman Zamani","H. Andrew Schwartz","Johannes Eichstaedt","Sharath Chandra Guntuku","Adithya Virinchipuram Ganesan","Sean Clouston","Salvatore Giorgi"],"abstract":"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.","url_abs":"https://aclanthology.org/2020.nlpcss-1.21","url_pdf":"https://aclanthology.org/2020.nlpcss-1.21.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"understanding-weekly-covid-19-concerns","repo_url":"https://github.com/wwbp/weekly_covid_lda_topics","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}