Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Previously submitted to: JMIR Medical Informatics (no longer under consideration since Jul 21, 2026)

Date Submitted: Dec 27, 2025

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

The Digital Landscape of Hypothyroidism on YouTube: A Comprehensive Analysis of Viewing Trends, Sentiment, Network Dynamics, and Content Creator Influence

  • Mehmet Poyrazer; 
  • Ayşe Münevver Mühürdaroğlu; 
  • Mahmut Emin Çelik; 
  • Püren Gökbulut; 
  • Sevde Nur Fırat

ABSTRACT

Background:

Hypothyroidism affects approximately 5% of the global population. While YouTube has emerged as a dominant health information platform, the specific network dynamics and information flow regarding chronic thyroid disease remain largely unexplored in digital health research.

Objective:

The objective of this study was to conduct a comprehensive infodemiological analysis of hypothyroidism content on YouTube, evaluating temporal trends, network structures, and audience sentiment across different creator categories.

Methods:

We identified 331 hypothyroidism-related YouTube videos (2015-2025). For creator-type comparisons, we excluded 18 videos from channels with unknown creator classification, yielding a filtered dataset of 313 videos (medical n=251; influencer n=62). Descriptive analyses used the full dataset (N=331), whereas inferential comparisons used the filtered dataset (n=313).The methodology integrated engagement analytics, The Valence Aware Dictionary and sEntiment Reasoner(VADER) sentiment analysis (validated via manual coding, κ=0.87), and social network analysis using NetworkX to construct bipartite graphs of the information ecosystem. Statistical comparisons were performed using Mann-Whitney U tests and Cohen d effect sizes.

Results:

Medical channels dominated content production (80.2%). Despite numerical differences in raw metrics, medical and influencer content demonstrated equivalent engagement rates (2.24% vs 2.36%; P=.31; d=-0.091), suggesting that engagement quality is independent of creator credentials. The COVID-19 pandemic catalyzed a 234% increase in content volume, resulting in a sustained elevation of post-pandemic engagement (2.47% vs 1.70% pre-pandemic). Network analysis revealed distinct structural models: medical networks followed a distributed expertise model (density=0.0151), whereas influencer networks utilized hub-centric approaches (density=0.0368). Sentiment analyses included 40,260 comments in the full dataset; creator-type stratified analyses included 38,495 comments after excluding unknown channels. Overall sentiment was predominantly positive (47.3%), though mean sentiment scores significantly declined from pre-pandemic optimism (0.270) to post-pandemic skepticism (0.183). Key information hubs identified included Dr. Westin Childs (medical centrality=0.189) and Fit Tuber (8.1M subscribers).

Conclusions:

This study demonstrates that engagement quality in thyroid health communication transcends traditional creator credentials. The pandemic has permanently transformed digital health consumption patterns, establishing YouTube as a resilient resource for chronic disease information while challenging prevailing assumptions regarding the dominance of health misinformation.


 Citation

Please cite as:

Poyrazer M, Mühürdaroğlu AM, Çelik ME, Gökbulut P, Fırat SN

The Digital Landscape of Hypothyroidism on YouTube: A Comprehensive Analysis of Viewing Trends, Sentiment, Network Dynamics, and Content Creator Influence

JMIR Preprints. 27/12/2025:90401

DOI: 10.2196/preprints.90401

URL: https://preprints.jmir.org/preprint/90401

Download PDF


Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.