Evidence map›Paper›PMID 30217792›Full record

ArticleJournal of medical Internet research2018

Discordance Between Human Papillomavirus Twitter Images and Disparities in Human Papillomavirus Risk and Disease in the United States: Mixed-Methods Analysis.

Yuki Lama, Tao Chen, Mark Dredze, Amelia Jamison, Sandra Crouse Quinn, David A Broniatowski

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
2.4field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Infodemiology and Infoveillance: Scoping Review.Journal of medical Internet research · 2020
    Article
  10. Quantifying COVID-19 Content in the Online Health Opinion War Using Machine Learning.IEEE access : practical innovations, open solutions · 2020
    Article
  11. Article
  12. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

Yuki LamaDepartment of Family Science, School of Public Health, University of Maryland, College Park, College Park, MD, United States.ORCID 0000-0003-2875-4093
Tao ChenCenter for Language and Speech Processing, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-5334-219X
Mark DredzeCenter for Language and Speech Processing, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-0422-2474
Amelia JamisonCenter for Health Equity, School of Public Health, University of Maryland, College Park, College Park, MD, United States.ORCID 0000-0002-3954-9581
Sandra Crouse QuinnDepartment of Family Science, School of Public Health, University of Maryland, College Park, College Park, MD, United States.ORCID 0000-0002-3258-4595
David A BroniatowskiDepartment of Engineering Management and Systems Engineering, The George Washington University, Washington DC, DC, United States.ORCID 0000-0002-3302-9497
University of Maryland, College Park · USJohns Hopkins University · USGeorge Washington University · US

Funding

Supplementing Survey-Based Analyses of Group Vaccination Narratives and Behaviors Using Social MediaR01GM114771 · NIGMS · GEORGE WASHINGTON UNIVERSITY · PI BRONIATOWSKI, DAVID ANDRE, QUINN, SANDRA CROUSE · 2015 to 2019
$1.6M
NIGMS NIH HHS R01 GM114771
6 · The paper itself

Abstract

backgroundRacial and ethnic minorities are disproportionately affected by human papillomavirus (HPV)-related cancer, many of which could have been prevented with vaccination. Yet, the initiation and completion rates of HPV vaccination remain low among these populations. Given the importance of social media platforms for health communication, we examined US-based HPV images on Twitter. We explored inconsistencies between the demographics represented in HPV images and the populations that experience the greatest burden of HPV-related disease.

objectiveThe objective of our study was to observe whether HPV images on Twitter reflect the actual burden of disease by select demographics and determine to what extent Twitter accounts utilized images that reflect the burden of disease in their health communication messages.

methodsWe identified 456 image tweets about HPV that contained faces posted by US users between November 11, 2014 and August 8, 2016. We identified images containing at least one human face and utilized Face++ software to automatically extract the gender, age, and race of each face. We manually annotated the source accounts of these tweets into 3 types as follows: government (38/298, 12.8%), organizations (161/298, 54.0%), and individual (99/298, 33.2%) and topics (news, health, and other) to examine how images varied by message source.

resultsFindings reflected the racial demographics of the US population but not the disease burden (795/1219, 65.22% white faces; 140/1219, 11.48% black faces; 71/1219, 5.82% Asian faces; and 213/1219, 17.47% racially ambiguous faces). Gender disparities were evident in the image faces; 71.70% (874/1219) represented female faces, whereas only 27.89% (340/1219) represented male faces. Among the 11-26 years age group recommended to receive HPV vaccine, HPV images contained more female-only faces (214/616, 34.3%) than males (37/616, 6.0%); the remainder of images included both male and female faces (365/616, 59.3%). Gender and racial disparities were present across different image sources. Faces from government sources were more likely to depict females (n=44) compared with males (n=16). Of male faces, 80% (12/15) of youth and 100% (1/1) of adults were white. News organization sources depicted high proportions of white faces (28/38, 97% of female youth and 12/12, 100% of adult males). Face++ identified fewer faces compared with manual annotation because of limitations with detecting multiple, small, or blurry faces. Nonetheless, Face++ achieved a high degree of accuracy with respect to gender, race, and age compared with manual annotation.

conclusionsThis study reveals critical differences between the demographics reflected in HPV images and the actual burden of disease. Racial minorities are less likely to appear in HPV images despite higher rates of HPV incidence. Health communication efforts need to represent populations at risk better if we seek to reduce disparities in HPV infection.

Indexed as

AdolescentAdultFemaleHealth CommunicationHumansMalePapillomaviridaePapillomavirus InfectionsRisk FactorsSocial MediaUnited StatesYoung Adultdisparitieshealth communicationHPV vaccinesimage tweetpublic health communicationsocial mediaTwittervisual communication

Identifiers

PMID30217792
PMCPMC6231890
OpenAlexW2811095053

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.