Literature DB >> 25045598

LESSONS LEARNED ABOUT PUBLIC HEALTH FROM ONLINE CROWD SURVEILLANCE.

Shawndra Hill1, Raina Merchant2, Lyle Ungar3.   

Abstract

The Internet has forever changed the way people access information and make decisions about their healthcare needs. Patients now share information about their health at unprecedented rates on social networking sites such as Twitter and Facebook and on medical discussion boards. In addition to explicitly shared information about health conditions through posts, patients reveal data on their inner fears and desires about health when searching for health-related keywords on search engines. Data are also generated by the use of mobile phone applications that track users' health behaviors (e.g., eating and exercise habits) as well as give medical advice. The data generated through these applications are mined and repackaged by surveillance systems developed by academics, companies, and governments alike to provide insight to patients and healthcare providers for medical decisions. Until recently, most Internet research in public health has been surveillance focused or monitoring health behaviors. Only recently have researchers used and interacted with the crowd to ask questions and collect health-related data. In the future, we expect to move from this surveillance focus to the "ideal" of Internet-based patient-level interventions where healthcare providers help patients change their health behaviors. In this article, we highlight the results of our prior research on crowd surveillance and make suggestions for the future.

Entities:  

Year:  2013        PMID: 25045598      PMCID: PMC4102381          DOI: 10.1089/big.2013.0020

Source DB:  PubMed          Journal:  Big Data        ISSN: 2167-6461            Impact factor:   2.128


  47 in total

1.  medpie: an information extraction package for medical message board posts.

Authors:  A Benton; J H Holmes; S Hill; A Chung; L Ungar
Journal:  Bioinformatics       Date:  2012-01-19       Impact factor: 6.937

2.  The utility of "Google Trends" for epidemiological research: Lyme disease as an example.

Authors:  Ari Seifter; Alison Schwarzwalder; Kate Geis; John Aucott
Journal:  Geospat Health       Date:  2010-05       Impact factor: 1.212

3.  Participatory epidemiology: use of mobile phones for community-based health reporting.

Authors:  Clark C Freifeld; Rumi Chunara; Sumiko R Mekaru; Emily H Chan; Taha Kass-Hout; Anahi Ayala Iacucci; John S Brownstein
Journal:  PLoS Med       Date:  2010-12-07       Impact factor: 11.069

4.  Seasonality in seeking mental health information on Google.

Authors:  John W Ayers; Benjamin M Althouse; Jon-Patrick Allem; J Niels Rosenquist; Daniel E Ford
Journal:  Am J Prev Med       Date:  2013-05       Impact factor: 5.043

5.  Using crowdsourcing technology for testing multilingual public health promotion materials.

Authors:  Anne M Turner; Katrin Kirchhoff; Daniel Capurro
Journal:  J Med Internet Res       Date:  2012-06-04       Impact factor: 5.428

6.  Increased Diels-Alderase activity through backbone remodeling guided by Foldit players.

Authors:  Christopher B Eiben; Justin B Siegel; Jacob B Bale; Seth Cooper; Firas Khatib; Betty W Shen; Foldit Players; Barry L Stoddard; Zoran Popovic; David Baker
Journal:  Nat Biotechnol       Date:  2012-01-22       Impact factor: 54.908

7.  Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance.

Authors:  Emily H Chan; Vikram Sahai; Corrie Conrad; John S Brownstein
Journal:  PLoS Negl Trop Dis       Date:  2011-05-31

8.  Temporal patterns of happiness and information in a global social network: hedonometrics and Twitter.

Authors:  Peter Sheridan Dodds; Kameron Decker Harris; Isabel M Kloumann; Catherine A Bliss; Christopher M Danforth
Journal:  PLoS One       Date:  2011-12-07       Impact factor: 3.240

9.  The Twitter of Babel: mapping world languages through microblogging platforms.

Authors:  Delia Mocanu; Andrea Baronchelli; Nicola Perra; Bruno Gonçalves; Qian Zhang; Alessandro Vespignani
Journal:  PLoS One       Date:  2013-04-18       Impact factor: 3.240

10.  Crowdsourcing malaria parasite quantification: an online game for analyzing images of infected thick blood smears.

Authors:  Miguel Angel Luengo-Oroz; Asier Arranz; John Frean
Journal:  J Med Internet Res       Date:  2012-11-29       Impact factor: 5.428

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  6 in total

1.  Fueling Hope: Stem Cells in Social Media.

Authors:  Julie M Robillard; Emanuel Cabral; Craig Hennessey; Brian K Kwon; Judy Illes
Journal:  Stem Cell Rev Rep       Date:  2015-08       Impact factor: 5.739

Review 2.  Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives. Contribution of the IMIA Social Media Working Group.

Authors:  M M Hansen; T Miron-Shatz; A Y S Lau; C Paton
Journal:  Yearb Med Inform       Date:  2014-08-15

3.  Characterizing the Discussion of Antibiotics in the Twittersphere: What is the Bigger Picture?

Authors:  Rachel Lynn Kendra; Suman Karki; Jesse Lee Eickholt; Lisa Gandy
Journal:  J Med Internet Res       Date:  2015-06-19       Impact factor: 5.428

4.  Exploring Spanish health social media for detecting drug effects.

Authors:  Isabel Segura-Bedmar; Paloma Martínez; Ricardo Revert; Julián Moreno-Schneider
Journal:  BMC Med Inform Decis Mak       Date:  2015-06-15       Impact factor: 2.796

5.  Characterizing Tweet Volume and Content About Common Health Conditions Across Pennsylvania: Retrospective Analysis.

Authors:  Christopher Tufts; Daniel Polsky; Kevin G Volpp; Peter W Groeneveld; Lyle Ungar; Raina M Merchant; Arthur P Pelullo
Journal:  JMIR Public Health Surveill       Date:  2018-12-06

6.  Tweet content related to sexually transmitted diseases: no joking matter.

Authors:  Elia Gabarron; J Artur Serrano; Rolf Wynn; Annie Y S Lau
Journal:  J Med Internet Res       Date:  2014-10-06       Impact factor: 5.428

  6 in total

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