Open AccessCharacterizing Online Media on COVID-19 during the Early Months of the Pandemic
Author(s) -
Henry K. Dambanemuya,
Haomin Lin,
Ágnes Horvát
Publication year - 2021
Publication title -
journal of quantitative description: digital media
Language(s) - English
Resource type - Journals
ISSN - 2673-8813
DOI - 10.51685/jqd.2021.014
Subject(s) - pandemic , social media , social distance , information sharing , data sharing , covid-19 , descriptive statistics , information dissemination , computer science , public health , internet privacy , data science , public relations , psychology , world wide web , political science , medicine , infectious disease (medical specialty) , disease , alternative medicine , mathematics , pathology , nursing , statistics
The 2019 coronavirus disease had wide-ranging effects on public health throughout the world. Vital in managing its spread was effective communication about public health guidelines such as social distancing and sheltering in place. Our study provides a descriptive analysis of online information sharing about coronavirus-related topics in 5.2 million English-language news articles, blog posts, and discussion forum entries shared in 197 countries during the early months of the pandemic. We illustrate potential approaches to analyze the data while emphasizing how often-overlooked dimensions of the online media environment play a crucial role in the observed information-sharing patterns. In particular, we show how the following three dimensions matter: (1) online media posts’ geographic location in relation to local exposure to the virus; (2) the platforms and types of media chosen for discussing various topics; and (3) temporal variations in information-sharing patterns. Our descriptive analyses of the multimedia data suggest that studies that overlook these crucial aspects of online media may arrive at misleading conclusions about the observed information-sharing patterns. This could impact the success of potential communication strategies devised based on data from online media. Our work has broad implications for the study and design of computational approaches for characterizing large-scale information dissemination during pandemics and beyond.