A scoping review of the use of Twitter for public health research

被引:84
作者
Edo-Osagie, Oduwa [1 ]
De La Iglesia, Beatriz [1 ]
Lake, Iain [2 ]
Edeghere, Obaghe [3 ]
机构
[1] Univ East Anglia, Sch Comp Sci, Norwich NR4 7TJ, Norfolk, England
[2] Univ East Anglia, Sch Environm Sci, Norwich NR4 7TJ, Norfolk, England
[3] Publ Hlth England, Natl Infect Serv, Birmingham B3 2PW, W Midlands, England
基金
英国经济与社会研究理事会;
关键词
Public health; Syndromic surveillance; Pharmacovigilance; Event forecasting; Disease tracking; SOCIAL MEDIA; SYNDROMIC SURVEILLANCE; FOODBORNE ILLNESS; MENTAL-HEALTH; EPIDEMIOLOGY; SENTIMENT; TWEETS; FLU;
D O I
10.1016/j.compbiomed.2020.103770
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Public health practitioners and researchers have used traditional medical databases to study and understand public health for a long time. Recently, social media data, particularly Twitter, has seen some use for public health purposes. Every large technological development in history has had an impact on the behaviour of society. The advent of the internet and social media is no different. Social media creates public streams of communication, and scientists are starting to understand that such data can provide some level of access into the people's opinions and situations. As such, this paper aims to review and synthesize the literature on Twitter applications for public health, highlighting current research and products in practice. A scoping review methodology was employed and four leading health, computer science and cross-disciplinary databases were searched. A total of 755 articles were retreived, 92 of which met the criteria for review. From the reviewed literature, six domains for the application of Twitter to public health were identified: (i) Surveillance; (ii) Event Detection; (iii) Pharmaco-vigilance; (iv) Forecasting; (v) Disease Tracking; and (vi) Geographic Identification. From our review, we were able to obtain a clear picture of the use of Twitter for public health. We gained insights into interesting observations such as how the popularity of different domains changed with time, the diseases and conditions studied and the different approaches to understanding each disease, which algorithms and techniques were popular with each domain, and more.
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页数:13
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