Peptide Market Intelligence Scraper

Track GLP-1s, BPC-157, and 30+ peptide compounds across Reddit, YouTube, and Amazon with sentiment, intent, and vendor leaderboards.

What does Peptide Market Intelligence do?

This actor aggregates peptide-related consumer conversation from Reddit, YouTube, and Amazon in a single run, then enriches every matched mention with compound disambiguation, sentiment, intent classification, and vendor/brand extraction via Gemini 2.5 Flash.

It produces a unified dataset plus aggregate Key-Value Store reports for supplement DTC brands, peptide market researchers, and market-intelligence teams tracking consumer demand around compounds such as semaglutide, tirzepatide, retatrutide, BPC-157, TB-500, MK-677, CJC-1295, GHK-Cu, and related aliases.

Target sources:

• Reddit posts
• YouTube video search results
• Amazon product search results

TikTok is available only as an experimental opt-in and is off by default.

Why use this actor?

• Unified schema - one mention row shape across Reddit, YouTube, and Amazon, joined by canonical compound.
• Noise filtered out - peptide-keyword hits that do not contain a resolved compound are dropped before billing.
• Compound disambiguation - slang and brand names such as sema, tirz, Ozempic, Wegovy, and Mounjaro collapse to canonical compound names.
• Vendor leaderboards - see which consumer peptide retailers are discussed, broken down by sentiment when Gemini enrichment is enabled.
• Trending compound deltas - set a stable datasetName across scheduled runs to get week-over-week mention-count changes per compound.
• Auto-discovery - Gemini can flag compound-like terms that are not yet in the vocabulary for later review.

How to use

1. Open the input form.
2. Pick sources. Default is YouTube and Amazon. Add Reddit when you want extra coverage and can tolerate occasional 403s from Apify cloud.
3. Optionally filter by compoundCategories.
4. Start with maxItemsPerSource: 30 to preview before scaling up.
5. Keep enrichmentMode: "full" for Gemini sentiment, intent, vendor, and brand extraction.
6. Open the Dataset tab for individual mentions and the Key-Value Store tab for aggregate reports.

Input

Example production input:

{
  "sources": ["youtube", "amazon"],
  "compoundCategories": ["GLP-1", "healing"],
  "maxItemsPerSource": 25,
  "enrichmentMode": "full",
  "includeAggregates": true
}

Example broad input:

{
  "sources": ["reddit", "youtube", "amazon"],
  "compoundCategories": ["GLP-1", "healing"],
  "customVocabulary": ["orforglipron"],
  "maxItemsPerSource": 500,
  "enrichmentMode": "full",
  "includeAggregates": true,
  "autoDiscover": false,
  "datasetName": "peptide-intel-weekly"
}

Output

Dataset row, one per matched mention:

{
  "id": "youtube:video:TX5lzmQLg30",
  "source": "youtube",
  "sourceUrl": "https://youtube.com/watch?v=TX5lzmQLg30&lc=video:TX5lzmQLg30",
  "nativeId": "video:TX5lzmQLg30",
  "text": "BPC-157 Explained: The Peptide That Speeds Up Recovery...",
  "author": "Jonathan Schoeff",
  "createdAt": "2026-05-01T11:30:34.711Z",
  "engagement": {},
  "context": {
    "videoId": "TX5lzmQLg30",
    "videoTitle": "BPC-157 Explained: The Peptide That Speeds Up Recovery"
  },
  "compounds": ["BPC-157"],
  "compoundCategory": "healing",
  "compoundAliases": ["BPC-157", "Bpc 157"],
  "sentiment": { "label": "neutral", "score": 0.9 },
  "intent": "other",
  "vendorMentions": [],
  "brandMentions": [],
  "enriched": true
}

Key-Value Store reports:

• TOP_COMPOUNDS - mention count per compound, overall and per source
• VENDOR_LEADERBOARD - top consumer peptide vendors with sentiment breakdown
• SENTIMENT_BY_COMPOUND - label distribution per compound for brand-risk monitoring
• TRENDING_COMPOUNDS_7D - week-over-week deltas when datasetName is set across scheduled runs
• SUGGESTED_VOCABULARY - new compound candidates when autoDiscover: true
• RUN_SUMMARY - per-source counts, enrichment stats, event totals

Data fields

Field	Description
id	Stable dedupe key: ${source}:${nativeId}
source	reddit, youtube, amazon, or experimental tiktok
sourceUrl	Canonical URL back to the item
text	Source text normalized into a single field
author	Username, handle, channel, or public author when available
createdAt	ISO-8601 UTC timestamp
engagement.upvotes	Reddit ups, YouTube likes, or Amazon helpful votes when available
engagement.rating	Amazon 1-5 star rating when available
context.subreddit	Reddit-only context
context.videoId	YouTube-only context
context.asin	Amazon-only context
compounds	Canonical compound names
compoundCategory	GLP-1, healing, GH-GHRP, cosmetic, sexual, nootropic, or longevity
compoundAliases	Raw alias/slang hits from the text
sentiment.label	positive, neutral, or negative
sentiment.score	0..1 confidence
intent	vendor-question, experience-report, dosage-question, side-effect, price-inquiry, or other
vendorMentions	Consumer peptide retailer names
brandMentions	Drug brand names such as Ozempic, Wegovy, and Mounjaro
enriched	true if Gemini processed this row

Pricing and cost estimation

Pay-per-event pricing:

• Actor start - $0.01 per run
• Peptide mention - $0.01 per enriched mention row
• Aggregate report - $0.03 per Key-Value Store aggregate doc, up to 4 charged aggregate docs per normal run

The first 50 chargeable events per run are free. A small preview run with maxItemsPerSource: 30 usually stays mostly or fully inside the free event allowance after the actor start fee.

A typical weekly scan with maxItemsPerSource: 500 across all active sources may produce around 600 enriched mentions after the regex noise filter, costing roughly $6.13 per run: $0.01 actor start, about $6.00 for mentions, and about $0.12 for aggregate reports. Heavier runs with maxItemsPerSource: 5000 may produce around 6,000 enriched mentions for about $60.13.

Tips and advanced options

• Start small: use maxItemsPerSource: 30 and one or two sources for a preview run.
• Named dataset for deltas: set datasetName to something stable, such as peptide-weekly, and schedule the actor.
• Customize the consumer overlay: edit the VOCAB_CONSUMER_OVERLAY Key-Value Store record to add slang, brand names, or experimental compounds without redeploying.
• Auto-discovery loop: run with autoDiscover: true, review SUGGESTED_VOCABULARY, then promote good candidates into customVocabulary or the overlay record.
• Raw mode: set enrichmentMode: "raw" to skip Gemini enrichment. It is faster, but sentiment, intent, vendor, and brand fields will be empty.

FAQ, disclaimers, and support

• Is scraping this legal? Reddit, YouTube, and Amazon all have terms of service. You are responsible for ensuring your use case complies with each platform's ToS and applicable law.
• No medical advice. Mentions extracted from consumer platforms are user-generated and not medical advice. This actor is a market-intelligence tool, not a clinical reference.
• Known limitations: Google Trends is currently disabled because its public endpoints return rate limits/HTML too often for reliable production runs. Reddit can return 403 from Apify cloud in some runs. TikTok is experimental and off by default. Amazon vendor mentions are rare because retailers often obfuscate peptide names.
• Support: report issues or feature requests via the Issues tab on this actor's page.