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Monitoring the Relationship between Social Network Status and Influenza Based on Social Media Data

Published online by Cambridge University Press:  18 September 2023

Qi Yan ,
Siqing Shan ,
Baishang Zhang ,
Weize Sun ,
Menghan Sun ,
Yiting Luo ,
Feng Zhao  and
Xiaoshuang Guo
Qi Yan
Affiliation:
Management School, Tianjin Normal University, Tianjin, China
Siqing Shan*
Affiliation:
School of Economics and Management, Beihang University, Beijing, China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing, China
Baishang Zhang
Affiliation:
Development Research Center of State Administration for Market Regulation of the PR China, Beijing, China
Weize Sun
Affiliation:
School of Economics and Management, Beihang University, Beijing, China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing, China
Menghan Sun
Affiliation:
School of Economics and Management, Beihang University, Beijing, China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing, China
Yiting Luo
Affiliation:
School of Economics and Management, Beihang University, Beijing, China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing, China
Feng Zhao
Affiliation:
School of Economics and Management, Beihang University, Beijing, China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing, China
Xiaoshuang Guo
Affiliation:
School of Economics and Management, Beihang University, Beijing, China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing, China
*
Corresponding author: Siqing Shan; Email: shansiqing@buaa.edu.cn.
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Abstract

Background:

This article aims to analyze the relationship between user characteristics on social networks and influenza.

Methods:

Three specific research questions are investigated: (1) we classify Weibo updates to recognize influenza-related information based on machine learning algorithms and propose a quantitative model for influenza susceptibility in social networks; (2) we adopt in-degree indicator from complex networks theory as social media status to verify its coefficient correlation with influenza susceptibility; (3) we also apply the LDA topic model to explore users’ physical condition from Weibo to further calculate its coefficient correlation with influenza susceptibility. From the perspective of social networking status, we analyze and extract influenza-related information from social media, with many advantages including efficiency, low cost, and real time.

Results:

We find a moderate negative correlation between the susceptibility of users to influenza and social network status, while there is a significant positive correlation between physical condition and susceptibility to influenza.

Conclusions:

Our findings reveal the laws behind the phenomenon of online disease transmission, and providing important evidence for analyzing, predicting, and preventing disease transmission. Also, this study provides theoretical and methodological underpinnings for further exploration and measurement of more factors associated with infection control and public health from social networks.

Keywords

social network statusphysical conditioninfluenzapublic healthinformation model
Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 17 , 2023 , e490
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health

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