Health Claim Perception & Consumer Risk: Meal Replacement Case Study
Project Overview
I analyzed health-related comments on meal replacement products from YouTube videos. Used YouTube API v3 and Google Cloud to scrape 684 comments across 21 videos from 2020–2025.
Around 24% of comments mention health effects, like digestive issues, allergic reactions, or weight/metabolic concerns. This is a significant signal for brands to monitor and act on.
Automated sentiment tools can misclassify risk, so human oversight is critical before making marketing or safety decisions.
Business Insights: Consumer Health & Engagement
⚠ Health Signals
24%
of comments report health effects
Action: Monitor & improve labeling
🚨 Severe Cases
19
Higher-severity mentions
Action: Track & educate consumers
💡 Sensitive Themes
326
Eating disorder mentions
Action: Proactive communication
🌟 Positive Health
11.8%
Positive mentions
Action: Highlight in marketing
🔥 Engagement
28%
High engagement comments
Action: Activate top users
👥 Community Advocates
25
Repeat commenters
Action: Gather early feedback
🤖 AI Monitoring Caution
⚠️
AI misclassification risk
Action: Combine AI with human review
Key Findings
Side Effect Prevalence
- Health-related mentions: 199
- Affected comments: 164
- Share of all comments: ~24%
Distribution by Category
- Allergic / immune: 78 mentions
- Mild digestive: 44 mentions
- Weight & metabolic: 42 mentions
- Cognitive / neurological: 14 mentions
Higher-Severity Signals
- Severe digestive: 6 mentions
- Cardiovascular-related: 5 mentions
- Energy & mood-related: 8 mentions
Temporal Risk Analysis
- Anomaly detection: Enabled
- Spikes detected: None
- Pattern: Stable baseline
Data reflects consumer-reported experiences. Not medical advice. Use for trend and risk monitoring.
Temporal & Engagement Patterns
Peak Activity Month: 2021-01
Most Active Day: Tuesday
Most Active Hour (UTC): 21:00
Likes per comment are 0.66 correlated with exclamation mark count → more expressive comments get more engagement.
Negative health-related themes get the most engagement → possibly because negative review videos attract like-minded commenters or those seeking validation.
Current Status
Project is ongoing. Focus now: transcript collection, claim categorization, early risk analysis. Next: iterative modeling and evaluation.
Code & Resources
Full code, including scraping and risk models, is on GitHub.
View Code on GitHub