Dear Pandemic: A topic modeling analysis of COVID-19 information needs among readers of an online science communication campaign.

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

SUBMITTER: Golos AM 

PROVIDER: S-EPMC10062627 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature