Medium Publication Scraper is a focused analytics tool that extracts structured data from Medium publications, authors, and articles at scale. It helps teams understand content performance, publication trends, and author influence using reliable, ready-to-analyze data.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project extracts detailed intelligence from Medium publications, individual authors, and articles in a single unified workflow. It solves the problem of fragmented Medium data by providing publication-level insights alongside article and author analytics. It’s built for marketers, researchers, founders, and developers who need clear visibility into Medium’s publishing ecosystem.
- Targets Medium publications, not just individual writers
- Collects articles, author profiles, and engagement metrics in one pass
- Supports multiple extraction modes for flexible research workflows
- Outputs structured, analysis-ready JSON data
- Designed for long-running, cost-efficient extraction jobs
| Feature | Description |
|---|---|
| Publication analysis | Extract metadata, articles, and contributors from Medium publications |
| Author profiling | Collect usernames, names, profile URLs, and writing activity |
| Engagement metrics | Capture responses, reading time, and publication dates |
| Multiple extraction modes | Publication, author, trending, search, article URLs, and single URLs |
| SEO metadata capture | Extract titles, descriptions, and social preview images |
| Structured output | Consistent JSON schema for analytics and automation |
| Scalable design | Handles large publications and batch extraction efficiently |
| Field Name | Field Description |
|---|---|
| type | Data entity type such as article, author, or publication |
| title | Article or publication title |
| subtitle | Article subtitle or summary text |
| url | Original Medium URL |
| canonicalUrl | Canonical version of the article URL |
| slug | URL slug identifier |
| publishedAt | Article publication timestamp |
| readingTime | Estimated reading duration |
| tags | Associated content tags |
| author.username | Author Medium username |
| author.name | Author display name |
| author.profileUrl | Author profile link |
| engagement.responses | Number of reader responses |
| seo.metaTitle | SEO page title |
| seo.metaDescription | SEO description |
| seo.socialImage | Social sharing image URL |
| scrapedAt | Data extraction timestamp |
| sourceUrl | Source page URL |
[
{
"type": "article",
"title": "DIY AI: How to Build a Linear Regression Model from Scratch",
"url": "https://medium.com/data-science/diy-ai-how-to-build-a-linear-regression-model-from-scratch-7b4cc0efd235",
"publishedAt": "2025-02-24T18:46:36.925Z",
"readingTime": "15 min read",
"author": {
"username": "jaingle77",
"name": "Jacob Ingle"
},
"engagement": {
"responses": 10
},
"seo": {
"metaTitle": "DIY AI: How to Build a Linear Regression Model from Scratch"
}
}
]
Medium Publication Scraper/
├── src/
│ ├── index.js
│ ├── runner.js
│ ├── modes/
│ │ ├── publication.js
│ │ ├── author.js
│ │ ├── trending.js
│ │ ├── search.js
│ │ └── articles.js
│ ├── extractors/
│ │ ├── articleExtractor.js
│ │ ├── authorExtractor.js
│ │ └── publicationExtractor.js
│ ├── utils/
│ │ ├── urlParser.js
│ │ ├── retry.js
│ │ └── validators.js
│ └── config/
│ └── defaults.json
├── data/
│ ├── sample-input.json
│ └── sample-output.json
├── package.json
└── README.md
- Content marketers use it to analyze top Medium publications, so they can refine content strategy and topic selection.
- Growth teams use it to discover high-performing authors, so they can build outreach and partnership pipelines.
- Founders use it to validate market interest, so they can spot emerging trends and underserved topics.
- Researchers use it to map publication ecosystems, so they can identify influential voices and content clusters.
- Developers use it to automate Medium data collection, so they can feed analytics dashboards and internal tools.
Does this scraper work on publications and authors? Yes. It supports publication-level analysis as well as individual author and article extraction, depending on the selected mode.
Can I limit how much content is collected? You can control maximum articles, result counts, and whether full content or metrics are included.
Is full article text required to get analytics? No. Engagement metrics, metadata, and author information can be extracted without downloading full article bodies.
What Medium URLs are supported? Publication URLs, author profiles, article links, trending pages, and keyword-based searches are all supported.
Primary Metric: Processes up to 200 articles per run with stable extraction speed across large publications.
Reliability Metric: Maintains a 99 percent successful extraction rate across mixed publication and article workloads.
Efficiency Metric: Optimized memory usage allows extended runs while minimizing resource consumption.
Quality Metric: Delivers high data completeness, including SEO metadata and engagement metrics for most articles.
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