Document Type
Article
Publication Date
3-30-2023
Abstract
BACKGROUND: The COVID-19 pandemic was accompanied by an "infodemic"-an overwhelming excess of accurate, inaccurate, and uncertain information. The social media-based science communication campaign Dear Pandemic was established to address the COVID-19 infodemic, in part by soliciting submissions from readers to an online question box. Our study characterized the information needs of Dear Pandemic's readers by identifying themes and longitudinal trends among question box submissions.
METHODS: We conducted a retrospective analysis of questions submitted from August 24, 2020, to August 24, 2021. We used Latent Dirichlet Allocation topic modeling to identify 25 topics among the submissions, then used thematic analysis to interpret the topics based on their top words and submissions. We used t-Distributed Stochastic Neighbor Embedding to visualize the relationship between topics, and we used generalized additive models to describe trends in topic prevalence over time.
RESULTS: We analyzed 3839 submissions, 90% from United States-based readers. We classified the 25 topics into 6 overarching themes: 'Scientific and Medical Basis of COVID-19,' 'COVID-19 Vaccine,' 'COVID-19 Mitigation Strategies,' 'Society and Institutions,' 'Family and Personal Relationships,' and 'Navigating the COVID-19 Infodemic.' Trends in topics about viral variants, vaccination, COVID-19 mitigation strategies, and children aligned with the news cycle and reflected the anticipation of future events. Over time, vaccine-related submissions became increasingly related to those surrounding social interaction.
CONCLUSIONS: Question box submissions represented distinct themes that varied in prominence over time. Dear Pandemic's readers sought information that would not only clarify novel scientific concepts, but would also be timely and practical to their personal lives. Our question box format and topic modeling approach offers science communicators a robust methodology for tracking, understanding, and responding to the information needs of online audiences.
Recommended Citation
Golos, Aleksandra M; Guntuku, Sharath Chandra; Piltch-Loeb, Rachael; Leininger, Lindsey J; Simanek, Amanda M; Kumar, Aparna; Albrecht, Sandra S; Dowd, Jennifer Beam; Jones, Malia; and Buttenheim, Alison M, "Dear Pandemic: A topic modeling analysis of COVID-19 information needs among readers of an online science communication campaign." (2023). College of Nursing Faculty Papers & Presentations. Paper 119.
https://jdc.jefferson.edu/nursfp/119
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Geographic Location Question Box.csv (1 kB)
Full Topic Probability Distribution.csv (3360 kB)
Themes Topicss Top Words.csv (5 kB)
Research Process.tif (92 kB)
Coherences Scores.tiff (1218 kB)
Daily Submissions.tiff (3235 kB)
PubMed ID
36996093
Language
English
Comments
This article is the author's final published version in PLoS ONE, Volume 18, Issue 3, March 2023, Article number e0281773.
The published version is available at https://doi.org/10.1371/journal.pone.0281773.
Copyright © © 2023 Golos et al
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.