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Leveraging social media data for pandemic detection and prediction

Health and Fitness

Leveraging social media data for pandemic detection and prediction

B. Shi, W. Huang, et al.

This study by Boyang Shi, Weixiang Huang, Yuanyuan Dang, and Wenhui Zhou explores the powerful link between social media data and pandemic detection, specifically during the COVID-19 outbreak in China. By analyzing user-generated content on platforms like Sina Weibo, researchers uncovered that public sentiment significantly indicates outbreak severity. Dive into the predictive capabilities that stem from individual self-reporting on illness symptoms.

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Playback language: English
Abstract
This study investigates the use of social media data for pandemic detection and prediction. Using the COVID-19 pandemic in China as a case study, the researchers analyzed Sina Weibo data, Baidu Search Index, and external datasets to determine which social media information best reflects outbreak severity. Empirical and prediction models showed that public user activity, particularly negative sentiment in blog posts, strongly correlates with the ground truth. Content analysis revealed that this predictive capability stems from individual self-reporting of illness symptoms.
Publisher
Humanities and Social Sciences Communications
Published On
Aug 23, 2024
Authors
Boyang Shi, Weixiang Huang, Yuanyuan Dang, Wenhui Zhou
Tags
social media
pandemic detection
COVID-19
Sina Weibo
public sentiment
predictive models
user activity

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