ArticleProceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing2020
Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling.
Article in Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
8 citing papers in PubMed.
- "Do not inject our babies": a social listening analysis of public opinion about authorizing pediatric COVID-19 vaccines.Health affairs scholar · 2024Article
- User feedback on the NHS test & Trace Service during COVID-19: The use of machine learning to analyse free-text data from 37,914 England adults.Public health in practice (Oxford, England) · 2023Article
- Hot Topic Recognition of Health Rumors Based on Anti-Rumor Articles on the WeChat Official Account Platform: Topic Modeling.Journal of medical Internet research · 2023Article
- Dear Pandemic: A topic modeling analysis of COVID-19 information needs among readers of an online science communication campaign.PloS one · 2023Article
- Spatial prediction of COVID-19 pandemic dynamics in the United States.ISPRS international journal of geo-information · 2022Article
- The where and when of COVID-19: Using ecological and Twitter-based assessments to examine impacts in a temporal and community context.PloS one · 2022Article
- Topical analysis of migration coverage during lockdown in India by mainstream print media.PloS one · 2022Article
- Contrastive Lexical Diffusion Coefficient: Quantifying the Stickiness of the Ordinary.Proceedings of the ... International World-Wide Web Conference. International WWW Conference · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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 mobility and unemployment rate.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.