Tweeting back: predicting new cases of back pain with mass social media data

Hopin Lee; James H. McAuley; Markus Hubscher; Heidi G. Allen; Steven J. Kamper; G. Lorimer Moseley

doi:10.1093/jamia/ocv168

Tweeting back: predicting new cases of back pain with mass social media data

Hopin Lee, James H. McAuley, Markus Hubscher, Heidi G. Allen, Steven J. Kamper, G. Lorimer Moseley

Epidemiology and Data Science

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

Original language	English
Pages (from-to)	644-648
Journal	Journal of the American Medical Informatics Association
Volume	23
Issue number	3
DOIs	https://doi.org/10.1093/jamia/ocv168
Publication status	Published - May 2016

Access to Document

10.1093/jamia/ocv168

Cite this

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title = "Tweeting back: predicting new cases of back pain with mass social media data",

keywords = "back pain, Twitter, public health, social media, case-crossover",

author = "Hopin Lee and McAuley, {James H.} and Markus Hubscher and Allen, {Heidi G.} and Kamper, {Steven J.} and Moseley, {G. Lorimer}",

year = "2016",

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language = "English",

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AU - Allen, Heidi G.

AU - Kamper, Steven J.

AU - Moseley, G. Lorimer

PY - 2016/5

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KW - back pain

KW - Twitter

KW - public health

KW - social media

KW - case-crossover

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DO - 10.1093/jamia/ocv168

M3 - Article

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VL - 23

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EP - 648

JO - Journal of the American Medical Informatics Association

JF - Journal of the American Medical Informatics Association

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