Literature DB >> 31595957

Social Media as an Emerging Data Resource for Epidemiologic Research: Characteristics of Regular and Nonregular Social Media Users in Nurses' Health Study II.

Eric S Kim1,2,3, Peter James4, Emily S Zevon1, Claudia Trudel-Fitzgerald1,2, Laura D Kubzansky1,2, Francine Grodstein5,6.   

Abstract

With advances in natural language processing and machine learning, researchers are leveraging social media as a low-cost, low-burden method for measuring various psychosocial factors. However, it is unclear whether information derived from social media is generalizable to broader populations, especially middle-aged and older adults. Using data on women aged 53-70 years from Nurses' Health Study II (2017-2018; n = 49,045), we assessed differences in sociodemographic characteristics, health conditions, behaviors, and psychosocial factors between regular and nonregular users of Facebook (Facebook, Inc., Menlo Park, California). We evaluated effect sizes with phi (φ) coefficients (categorical data) or Cohen's d (continuous data) and calculated odds ratios with 95% confidence intervals. While most comparisons between regular and nonregular users achieved statistical significance in this large sample, effect sizes were mostly "very small" (conventionally defined as φ or d <0.01) (e.g., optimism score: meanregular users = 19 vs. meannonregular users = 19 (d = -0.03); physical activity: meanregular users = 24 metabolic equivalent of task (MET)-hours/week vs. meannonregular users = 24 MET-hours/week (d = 0.01)). Some factors had slightly larger differences for regular users versus nonregular users (e.g., depression: 28% vs. 23% (φ = 0.05); odds ratio = 1.27 (95% confidence interval: 1.22, 1.33); obesity: 34% vs. 26% (φ = 0.07); odds ratio = 1.42 (95% confidence interval: 1.36, 1.48)). Results suggest that regular Facebook users were similar to nonregular users across sociodemographic and psychosocial factors, with modestly worse health regarding obesity and depressive symptoms. In future research, investigators should evaluate other demographic groups.
© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  epidemiologic methods; machine learning; natural language processing; psychology; public health; social media

Mesh:

Year:  2020        PMID: 31595957      PMCID: PMC7156136          DOI: 10.1093/aje/kwz224

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  23 in total

1.  Facebook as a research tool for the social sciences: Opportunities, challenges, ethical considerations, and practical guidelines.

Authors:  Michal Kosinski; Sandra C Matz; Samuel D Gosling; Vesselin Popov; David Stillwell
Journal:  Am Psychol       Date:  2015-09

2.  The association between adolescent well-being and digital technology use.

Authors:  Amy Orben; Andrew K Przybylski
Journal:  Nat Hum Behav       Date:  2019-01-14

3.  Association of Facebook Use With Compromised Well-Being: A Longitudinal Study.

Authors:  Holly B Shakya; Nicholas A Christakis
Journal:  Am J Epidemiol       Date:  2017-02-01       Impact factor: 4.897

4.  Origin, Methods, and Evolution of the Three Nurses' Health Studies.

Authors:  Ying Bao; Monica L Bertoia; Elizabeth B Lenart; Meir J Stampfer; Walter C Willett; Frank E Speizer; Jorge E Chavarro
Journal:  Am J Public Health       Date:  2016-07-26       Impact factor: 9.308

5.  Ethical Issues in Social Media Research for Public Health.

Authors:  Ruth F Hunter; Aisling Gough; Niamh O'Kane; Gary McKeown; Aine Fitzpatrick; Tom Walker; Michelle McKinley; Mandy Lee; Frank Kee
Journal:  Am J Public Health       Date:  2018-01-18       Impact factor: 9.308

6.  Living in the Past, Present, and Future: Measuring Temporal Orientation With Language.

Authors:  Gregory Park; H Andrew Schwartz; Maarten Sap; Margaret L Kern; Evan Weingarten; Johannes C Eichstaedt; Jonah Berger; David J Stillwell; Michal Kosinski; Lyle H Ungar; Martin E P Seligman
Journal:  J Pers       Date:  2016-02-29

7.  Validity of self-reported waist and hip circumferences in men and women.

Authors:  E B Rimm; M J Stampfer; G A Colditz; C G Chute; L B Litin; W C Willett
Journal:  Epidemiology       Date:  1990-11       Impact factor: 4.822

8.  The Facebook Experiment: Quitting Facebook Leads to Higher Levels of Well-Being.

Authors:  Morten Tromholt
Journal:  Cyberpsychol Behav Soc Netw       Date:  2016-11

9.  A brief measure for assessing generalized anxiety disorder: the GAD-7.

Authors:  Robert L Spitzer; Kurt Kroenke; Janet B W Williams; Bernd Löwe
Journal:  Arch Intern Med       Date:  2006-05-22

10.  Ethical challenges of big data in public health.

Authors:  Effy Vayena; Marcel Salathé; Lawrence C Madoff; John S Brownstein
Journal:  PLoS Comput Biol       Date:  2015-02-09       Impact factor: 4.475

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  2 in total

1.  Estimating geographic subjective well-being from Twitter: A comparison of dictionary and data-driven language methods.

Authors:  Kokil Jaidka; Salvatore Giorgi; H Andrew Schwartz; Margaret L Kern; Lyle H Ungar; Johannes C Eichstaedt
Journal:  Proc Natl Acad Sci U S A       Date:  2020-04-27       Impact factor: 11.205

2.  Linking Individual-Level Facebook Posts With Psychological and Health Data in an Epidemiological Cohort: Feasibility Study.

Authors:  Peter James; Claudia Trudel-Fitzgerald; Harold H Lee; Hayami K Koga; Laura D Kubzansky; Francine Grodstein
Journal:  JMIR Form Res       Date:  2022-04-07
  2 in total

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