Literature DB >> 29801949

A survey of social media data analysis for physical activity surveillance.

Sam Liu1, Sean D Young2.   

Abstract

Social media data can provide valuable information regarding people's behaviors and health outcomes. Previous studies have shown that social media data can be extracted to monitor and predict infectious disease outbreaks. These same approaches can be applied to other fields including physical activity research and forensic science. Social media data have the potential to provide real-time monitoring and prediction of physical activity level in a given region. This tool can be valuable to public health organizations as it can overcome the time lag in the reporting of physical activity epidemiology data faced by traditional research methods (e.g. surveys, observational studies). As a result, this tool could help public health organizations better mobilize and target physical activity interventions. The first part of this paper aims to describe current approaches (e.g. topic modeling, sentiment analysis and social network analysis) that could be used to analyze social media data to provide real-time monitoring of physical activity level. The second aim of this paper was to discuss ways to apply social media analysis to other fields such as forensic sciences and provide recommendations to further social media research.
Copyright © 2016 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Entities:  

Keywords:  Physical activity; Public health; Social media data analysis; Twitter

Mesh:

Year:  2016        PMID: 29801949      PMCID: PMC6276785          DOI: 10.1016/j.jflm.2016.10.019

Source DB:  PubMed          Journal:  J Forensic Leg Med        ISSN: 1752-928X            Impact factor:   1.614


  11 in total

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Journal:  Med Sci Sports Exerc       Date:  2007-08       Impact factor: 5.411

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Authors:  Sean D Young; Caitlin Rivers; Bryan Lewis
Journal:  Prev Med       Date:  2014-02-08       Impact factor: 4.018

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Authors:  Nicholas A Christakis; James H Fowler
Journal:  N Engl J Med       Date:  2007-07-25       Impact factor: 91.245

8.  Feasibility of using social networking technologies for health research among men who have sex with men: a mixed methods study.

Authors:  Sean D Young; Devan Jaganath
Journal:  Am J Mens Health       Date:  2013-02-12

9.  You Are What You Tweet: Connecting the Geographic Variation in America's Obesity Rate to Twitter Content.

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Journal:  PLoS One       Date:  2015-09-02       Impact factor: 3.240

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Authors:  David A Broniatowski; Michael J Paul; Mark Dredze
Journal:  PLoS One       Date:  2013-12-09       Impact factor: 3.240

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  5 in total

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3.  Analyzing sentiments and themes on cannabis in Canada using 2018 to 2020 Twitter data.

Authors:  Maisam Najafizada; Arifur Rahman; Jennifer Donnan; Zhihao Dong; Lisa Bishop
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4.  Over a decade of social opinion mining: a systematic review.

Authors:  Keith Cortis; Brian Davis
Journal:  Artif Intell Rev       Date:  2021-06-25       Impact factor: 8.139

5.  Geographic Differences in Cannabis Conversations on Twitter: Infodemiology Study.

Authors:  Jenna van Draanen; HaoDong Tao; Saksham Gupta; Sam Liu
Journal:  JMIR Public Health Surveill       Date:  2020-10-05
  5 in total

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