Visualizing YouTube Commenters' Conceptions of the US Health Care System: Semantic Network Analysis Method for...

Area of Science:

• Social media analytics
• Health policy research
• Network science

Background:

• Extracting meaningful insights from social media noise for decision-makers is a significant challenge.
• Understanding public discourse on complex topics like health care requires advanced analytical methods.

Purpose of the Study:

• To evaluate a semantic network method for visualizing social media discourse on the US health care system.
• To create an interactive visualization of public commentary on US health care.

Main Methods:

• Repurposed VOSviewer software to analyze 179,193 YouTube comments on US health care from 53 videos (2014-2023).
• Employed overlay-enhanced semantic network analysis to map commentary structure and content.
• Examined term co-occurrence, network clusters, and custom information layers (overlays).
• Assessed network robustness, representativeness, relevance, accuracy, and decision-support utility.

Main Results:

• Unveiled complex discussions on illness, wellness, health services, ideology, health care reform, market regulation, insurance, workforce, dental care, and wait times.
• Demonstrated thematic alignment between YouTube comment terms and eliciting videos, with discussions persisting over time.
• Identified international comparisons in health care costs and wait times, indicating a global discussion.
• Highlighted nurse shortages and burnout as significant concerns, with ideological differences noted between universal health care and single-payer system discussions.

Conclusions:

• YouTube users engage with critical policy issues: social determinants of health, vulnerable populations, health equity, racism, quality, and access.
• The presented semantic network method is versatile for health policy studies, supporting evidence-based decision-making and understanding diverse perspectives.
• Interactive visualizations facilitate uncovering large-scale patterns and guide qualitative research resource allocation.