Use of social media for the detection and analysis of infectious diseases in China

Xinyue Ye; Shengwen Li; Xining Yang; Chenglin Qin

doi:10.3390/ijgi5090156

Use of social media for the detection and analysis of infectious diseases in China

Xinyue Ye, Shengwen Li, Xining Yang, Chenglin Qin

Research output: Contribution to journal › Article › peer-review

58 Scopus citations

Abstract

Social media activity has become an important component of daily life for many people. Messages from Twitter (US) and Weibo (China) have shown their potential as important data sources for detecting and analyzing infectious diseases. Such emerging and dynamic new data sources allow us to predict how infectious diseases develop and evolve both spatially and temporally. We report the dynamics of dengue fever in China using messages fromWeibo. We first extract and construct a list of keywords related to dengue fever in order to analyze how frequently these words appear in Weibo messages based on the Latent Dirichlet Allocation (LDA). Spatial analysis is then applied to detect how dengue fever cases cluster spatially and spread over time.

Original language	English (US)
Article number	156
Journal	ISPRS International Journal of Geo-Information
Volume	5
Issue number	9
DOIs	https://doi.org/10.3390/ijgi5090156
State	Published - Sep 2016
Externally published	Yes

All Science Journal Classification (ASJC) codes

Geography, Planning and Development
Computers in Earth Sciences
Earth and Planetary Sciences (miscellaneous)

Keywords

China
Infectious disease
Social media
Space
Time

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10.3390/ijgi5090156

Cite this

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title = "Use of social media for the detection and analysis of infectious diseases in China",

abstract = "Social media activity has become an important component of daily life for many people. Messages from Twitter (US) and Weibo (China) have shown their potential as important data sources for detecting and analyzing infectious diseases. Such emerging and dynamic new data sources allow us to predict how infectious diseases develop and evolve both spatially and temporally. We report the dynamics of dengue fever in China using messages fromWeibo. We first extract and construct a list of keywords related to dengue fever in order to analyze how frequently these words appear in Weibo messages based on the Latent Dirichlet Allocation (LDA). Spatial analysis is then applied to detect how dengue fever cases cluster spatially and spread over time.",

keywords = "China, Infectious disease, Social media, Space, Time",

author = "Xinyue Ye and Shengwen Li and Xining Yang and Chenglin Qin",

note = "Funding Information: This work was supported by the National Science Foundation under Grant No. 1416509, National Social Science Foundation of China (No. 11 & ZD159), and the National Natural Science Foundation of China under Grant No. 41301426, 41301427, 41329001.",

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AU - Ye, Xinyue

AU - Li, Shengwen

AU - Yang, Xining

AU - Qin, Chenglin

N1 - Funding Information: This work was supported by the National Science Foundation under Grant No. 1416509, National Social Science Foundation of China (No. 11 & ZD159), and the National Natural Science Foundation of China under Grant No. 41301426, 41301427, 41329001.

PY - 2016/9

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N2 - Social media activity has become an important component of daily life for many people. Messages from Twitter (US) and Weibo (China) have shown their potential as important data sources for detecting and analyzing infectious diseases. Such emerging and dynamic new data sources allow us to predict how infectious diseases develop and evolve both spatially and temporally. We report the dynamics of dengue fever in China using messages fromWeibo. We first extract and construct a list of keywords related to dengue fever in order to analyze how frequently these words appear in Weibo messages based on the Latent Dirichlet Allocation (LDA). Spatial analysis is then applied to detect how dengue fever cases cluster spatially and spread over time.

AB - Social media activity has become an important component of daily life for many people. Messages from Twitter (US) and Weibo (China) have shown their potential as important data sources for detecting and analyzing infectious diseases. Such emerging and dynamic new data sources allow us to predict how infectious diseases develop and evolve both spatially and temporally. We report the dynamics of dengue fever in China using messages fromWeibo. We first extract and construct a list of keywords related to dengue fever in order to analyze how frequently these words appear in Weibo messages based on the Latent Dirichlet Allocation (LDA). Spatial analysis is then applied to detect how dengue fever cases cluster spatially and spread over time.

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KW - Social media

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KW - Time

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Use of social media for the detection and analysis of infectious diseases in China

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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