Literature DB >> 31830827

Rating the Valence of Media Content about Electronic Cigarettes Using Crowdsourcing: Testing Rater Instructions and Estimating the Optimal Number of Raters.

Stella Juhyun Lee1,2, Jiaying Liu3, Laura A Gibson4, Robert C Hornik5.   

Abstract

Electronic cigarettes (e-cigarettes) are a controversial public health topic due to their increasing popularity among youth and the uncertainty about their risks and benefits. Researchers have started to assess the valence of media content about e-cigarette use, mostly using expert coding. The current study aims to offer a methodological framework and guideline when using crowdsourcing to rate the valence of e-cigarette media content. Specifically, we present (1) an experiment to determine rating instructions that would result in reliable valence ratings and (2) an analysis to identify the optimal number of raters needed to replicate these ratings. Specifically, we compared ratings produced by crowdsourced raters instructed to rate from several different perspectives (e.g., objective vs. subjective) and determined the instructions that led to reliable ratings. We then used bootstrapping methods and a set of criteria to identify the minimum number of raters needed to replicate these ratings. Results suggested that when rating e-cigarette valence, instructing raters to rate from their own subjective perspective produced reliable results, and nine raters were deemed the optimal number of raters. We expect these findings to inform future content analyses of e-cigarette valence. The study procedures can be applied to crowdsourced content analyses of other health-related media content to determine appropriate rating instructions and the number of raters.

Entities:  

Mesh:

Year:  2019        PMID: 31830827      PMCID: PMC7292742          DOI: 10.1080/10410236.2019.1700882

Source DB:  PubMed          Journal:  Health Commun        ISSN: 1041-0236


  21 in total

1.  2 x 2 kappa coefficients: measures of agreement or association.

Authors:  D A Bloch; H C Kraemer
Journal:  Biometrics       Date:  1989-03       Impact factor: 2.571

2.  Reduced harm or another gateway to smoking? source, message, and information characteristics of E-cigarette videos on YouTube.

Authors:  Hye-Jin Paek; Sookyong Kim; Thomas Hove; Jung Yoon Huh
Journal:  J Health Commun       Date:  2013-10-11

3.  A Randomized Trial of the Effect of E-cigarette TV Advertisements on Intentions to Use E-cigarettes.

Authors:  Matthew C Farrelly; Jennifer C Duke; Erik C Crankshaw; Matthew E Eggers; Youn O Lee; James M Nonnemaker; Annice E Kim; Lauren Porter
Journal:  Am J Prev Med       Date:  2015-07-07       Impact factor: 5.043

4.  Exposure to Advertisements and Electronic Cigarette Use Among US Middle and High School Students.

Authors:  Tushar Singh; Israel T Agaku; René A Arrazola; Kristy L Marynak; Linda J Neff; Italia T Rolle; Brian A King
Journal:  Pediatrics       Date:  2016-05       Impact factor: 7.124

5.  Crowdsourced data collection for public health: A comparison with nationally representative, population tobacco use data.

Authors:  John D Kraemer; Andrew A Strasser; Eric N Lindblom; Raymond S Niaura; Darren Mays
Journal:  Prev Med       Date:  2017-07-08       Impact factor: 4.018

6.  Campaigns and counter campaigns: reactions on Twitter to e-cigarette education.

Authors:  Jon-Patrick Allem; Patricia Escobedo; Kar-Hai Chu; Daniel W Soto; Tess Boley Cruz; Jennifer B Unger
Journal:  Tob Control       Date:  2016-03-08       Impact factor: 7.552

Review 7.  How to Think-Not Feel-about Tobacco Harm Reduction.

Authors:  Kenneth E Warner
Journal:  Nicotine Tob Res       Date:  2019-09-19       Impact factor: 4.244

8.  Overlapping confidence intervals or standard error intervals: what do they mean in terms of statistical significance?

Authors:  Mark E Payton; Matthew H Greenstone; Nathaniel Schenker
Journal:  J Insect Sci       Date:  2003-10-30       Impact factor: 1.857

9.  Wanna know about vaping? Patterns of message exposure, seeking and sharing information about e-cigarettes across media platforms.

Authors:  Sherry L Emery; Lisa Vera; Jidong Huang; Glen Szczypka
Journal:  Tob Control       Date:  2014-07       Impact factor: 7.552

10.  Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning.

Authors:  Heather Cole-Lewis; Arun Varghese; Amy Sanders; Mary Schwarz; Jillian Pugatch; Erik Augustson
Journal:  J Med Internet Res       Date:  2015-08-25       Impact factor: 5.428

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  1 in total

1.  Valence of Media Coverage About Electronic Cigarettes and Other Tobacco Products From 2014 to 2017: Evidence From Automated Content Analysis.

Authors:  Kwanho Kim; Laura A Gibson; Sharon Williams; Yoonsang Kim; Steven Binns; Sherry L Emery; Robert C Hornik
Journal:  Nicotine Tob Res       Date:  2020-10-08       Impact factor: 4.244

  1 in total

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