Using Twitter for Public Health Surveillance from Monitoring and Prediction to Public Response
by
Sophie E. Jordan 1
, Sierra E. Hovet 2, Isaac Chun-Hai Fung 3, Hai Liang 4, King-Wa Fu 5 and Zion Tsz Ho Tse 2,*
Sophie E. Jordan 1, Sierra E. Hovet 2, Isaac Chun-Hai Fung 3, Hai Liang 4, King-Wa Fu 5 and Zion Tsz Ho Tse 2,*
1
School of Chemical, Materials, and Biomedical Engineering, College of Engineering, University of Georgia, Athens, GA 30602, USA
2
School of Electrical and Computer Engineering, College of Engineering, University of Georgia, Athens, GA 30602, USA
3
Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA 30460, USA
4
School of Journalism and Communication, Chinese University of Hong Kong, Hong Kong, China
5
Journalism and Media Studies Centre, The University of Hong Kong, Hong Kong, China
*
Author to whom correspondence should be addressed.
Data 2019, 4(1), 6; https://doi.org/10.3390/data4010006
Received: 14 December 2018 / Revised: 21 December 2018 / Accepted: 22 December 2018 / Published: 29 December 2018
(This article belongs to the Special Issue Big Data and Digital Health)
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.
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Keywords:
public health; Twitter; classification; data mining; Zika; Ebola
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MDPI and ACS Style
Jordan, S.E.; Hovet, S.E.; Fung, I. .-H.; Liang, H.; Fu, K.-W.; Tse, Z.T.H. Using Twitter for Public Health Surveillance from Monitoring and Prediction to Public Response. Data 2019, 4, 6.
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