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Infertility influencers: an analysis of information and influence in the fertility webspace
by
Smith, Meghan B
, Grifo, James A
, Blakemore, Jennifer K
, Bayer, Arielle H
in
Classification
/ Demography
/ Endometriosis
/ Fertility
/ Freezing
/ Immunoglobulins
/ Infertility
/ Social networks
/ Statistical analysis
2020
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Infertility influencers: an analysis of information and influence in the fertility webspace
by
Smith, Meghan B
, Grifo, James A
, Blakemore, Jennifer K
, Bayer, Arielle H
in
Classification
/ Demography
/ Endometriosis
/ Fertility
/ Freezing
/ Immunoglobulins
/ Infertility
/ Social networks
/ Statistical analysis
2020
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While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Infertility influencers: an analysis of information and influence in the fertility webspace
by
Smith, Meghan B
, Grifo, James A
, Blakemore, Jennifer K
, Bayer, Arielle H
in
Classification
/ Demography
/ Endometriosis
/ Fertility
/ Freezing
/ Immunoglobulins
/ Infertility
/ Social networks
/ Statistical analysis
2020
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Infertility influencers: an analysis of information and influence in the fertility webspace
Journal Article
Infertility influencers: an analysis of information and influence in the fertility webspace
2020
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Overview
PurposeTo examine fertility-related social media accounts and influencers on two social media platforms.MethodsThe search function of Twitter (TW) and Instagram (IG) was used to generate a list of accounts with the terms: fertility, infertility, ttc, egg freezing, ivf, endometriosis, and reproductive. Accounts not in English, in private, with no posts in > 1 year, or with content unrelated to search terms were excluded. Accounts were assessed for author type; REI board certification (REI-BC); influencer (INF) status (> 10 K followers on IG; verified check mark on TW); account demographics; and content in last 5 posts. Statistical analysis included unpaired t tests, a classification and regression tree (CART) analysis, and stepwise multiple logistic regression.ResultsSeven hundred ten accounts were identified and 537 (278 TW, 259 IG) were included. Account types included societies, clinics, physicians, patients, groups, and “other.” Instagram content (1290 posts reviewed) was primarily personal stories (31.7%) or inspiration/support (23.7%). Twitter content (1390 posts reviewed) was mostly promotion (28.2%) and research/education (20.2%). Thirty-nine accounts (12.5%) were influencers. Fertility influencers were most often awareness/support accounts (59.8% TW, 25.0% IG), patients (12.8% TW, 25% IG), or other (17.9% TW, 21.0% IG). Only 7.7% TW and 7.1% IG INFs were board-certified REI physicians. The best predictor for classification as an influencer was high activity (> 50 posts/month TW, > 10 posts/month IG).ConclusionAs patients increasingly utilize social media to obtain and engage with health information, it is critical to understand the fertility-related SM landscape. This understanding may help to successfully enhance relationships with patients and ensure dissemination of accurate information.
Publisher
Springer Nature B.V
Subject
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