
Substack Notes Scraper 🔍
2 hours trial then $19.99/month - No credit card required now

Substack Notes Scraper 🔍
2 hours trial then $19.99/month - No credit card required now
Extract notes and comments from Substack's search results with images, user info, and engagement metrics. Perfect for content analysis, user research, and tracking discussions around specific topics on Substack.
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1 monthly user
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>99% runs succeeded
Created in Feb 2025
Modified 8 days ago
Extract valuable content and engagement data from Substack Notes search results. This actor helps you gather insights about discussions, trends, and user engagement around specific topics on Substack's social platform.
🎯 Use Cases
- Monitor discussions around specific keywords or topics
- Track user engagement and content performance
- Analyze visual content trends through attached images
- Research user behavior and community interactions
- Gather social proof and testimonials
- Track competitor activities and engagement
✨ Features
- 🔎 Search-based data extraction
- 👤 Detailed user information (name, handle, bio)
- 📊 Engagement metrics (reactions, restacks, comments)
- 🖼️ Image attachments with dimensions
- ⏱️ Timestamp data for temporal analysis
- 🔄 Automatic pagination handling
- 🚀 Stealth mode to avoid detection
🔢 Output
The actor provides detailed JSON output including:
- User profile information
- Note content and metadata
- Engagement metrics
- Image attachments
- Timestamps
- Search context
💡 Input Parameters
keywords
(Array): List of keywords to search formaxItems
(Number): Maximum number of items to scrapeproxyConfiguration
(Object): Proxy settings (optional)
📌 Limitations
- Respects Substack's rate limits and terms of service
- Search results are limited to public notes
- Image downloads require additional processing
💪 Tips for Best Results
- Use specific keywords for targeted results
- Adjust
maxItems
based on your needs - Consider using proxies for large-scale scraping
- Allow sufficient run time for complete data collection
Input Example
A full explanation of an input example in JSON.
1{ 2 "keywords": [ 3 "sale" 4 ], 5 "maxItems": 50 6}
Output sample
The results will be wrapped into a dataset which you can always find in the Storage tab. Here's an excerpt from the data you'd get if you apply the input parameters above:
And here is the same data but in JSON. You can choose in which format to download your data: JSON, JSONL, Excel spreadsheet, HTML table, CSV, or XML.
1[ 2 { 3 "keyword": "sale", 4 "entityKey": "c-91995525", 5 "type": "comment", 6 "user": { 7 "id": 310653644, 8 "name": "Chris Sale", 9 "handle": "chrissalephoto", 10 "photoUrl": "https://substack-post-media.s3.amazonaws.com/public/images/b5673547-b3db-4089-9e43-f32170dba61b_96x96.jpeg", 11 "bio": "Landscape photographer based in Cumbria. Sharing stories from the Eden Valley and the North Pennines. " 12 }, 13 "comment": { 14 "id": 91995525, 15 "body": "Bothy | Melmerby Fell, North Pennines AONB | February 2025", 16 "date": "2025-02-08T14:19:40.888Z", 17 "reactionCount": 60, 18 "reactions": { 19 "❤": 60 20 }, 21 "restacks": 1, 22 "childrenCount": 5 23 }, 24 "attachments": [ 25 { 26 "id": "5a9aef05-696f-4bb5-a9bf-45807bcaf2da", 27 "type": "image", 28 "imageUrl": "https://substack-post-media.s3.amazonaws.com/public/images/8bc060ec-f0c6-43a4-b4b8-6ca80b54cec9_2160x2700.jpeg", 29 "imageWidth": 2160, 30 "imageHeight": 2700 31 } 32 ], 33 "scrapedAt": "2025-02-10T05:59:15.575Z" 34 }, 35 ... 36]
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