Using Twitter for Public Health Surveillance from Monitoring and Prediction to Public Response

Sophie E. Jordan, Sierra E. Hovet, Isaac Chun-Hai Fung, Hai Liang, King-Wa Fu and Zion Tsz Ho Tse
Additional contact information
Sophie E. Jordan: School of Chemical, Materials, and Biomedical Engineering, College of Engineering, University of Georgia, Athens, GA 30602, USA
Sierra E. Hovet: School of Electrical and Computer Engineering, College of Engineering, University of Georgia, Athens, GA 30602, USA
Isaac Chun-Hai Fung: Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA 30460, USA
Hai Liang: School of Journalism and Communication, Chinese University of Hong Kong, Hong Kong, China
King-Wa Fu: Journalism and Media Studies Centre, The University of Hong Kong, Hong Kong, China
Zion Tsz Ho Tse: School of Electrical and Computer Engineering, College of Engineering, University of Georgia, Athens, GA 30602, USA

Data, 2018, vol. 4, issue 1, 1-20

Abstract: Twitter is a social media platform where over 500 million people worldwide publish their ideas and discuss diverse topics, including their health conditions and public health events. Twitter has proved to be an important source of health-related information on the Internet, given the amount of information that is shared by both citizens and official sources. Twitter provides researchers with a real-time source of public health information on a global scale, and can be very important in public health research. Classifying Twitter data into topics or categories is helpful to better understand how users react and communicate. A literature review is presented on the use of mining Twitter data or similar short-text datasets for public health applications. Each method is analyzed for ways to use Twitter data in public health surveillance. Papers in which Twitter content was classified according to users or tweets for better surveillance of public health were selected for review. Only papers published between 2010–2017 were considered. The reviewed publications are distinguished by the methods that were used to categorize the Twitter content in different ways. While comparing studies is difficult due to the number of different methods that have been used for applying Twitter and interpreting data, this state-of-the-art review demonstrates the vast potential of utilizing Twitter for public health surveillance purposes.

Keywords: public health; Twitter; classification; data mining; Zika; Ebola (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/2306-5729/4/1/6/pdf (application/pdf)
https://www.mdpi.com/2306-5729/4/1/6/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:4:y:2018:i:1:p:6-:d:193848

Access Statistics for this article

Data is currently edited by Ms. Cecilia Yang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().