Medium Following Scraper

Medium Following Scraper collects structured data from Medium users’ following lists so you can understand who a creator follows and how networks form over time. It’s built for fast, repeatable collection of profile metadata that supports influencer research, audience analysis, and creator network mapping.

     

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Introduction

This project scrapes the “following” lists from public Medium profiles and returns a clean dataset of user-level details for each followed account. It solves the problem of manually browsing large following lists and losing track of who follows whom across multiple profiles. It’s designed for analysts, growth teams, researchers, and developers who need consistent Medium following data for downstream workflows.

Network Discovery & Profile Enrichment

• Extracts follow relationships starting from one or many Medium usernames.
• Captures normalized profile metadata (IDs, usernames, bios, images, and status flags).
• Supports batch runs with configurable limits for controlled sampling or large pulls.
• Produces output that’s easy to load into BI tools, CRMs, or graph analysis pipelines.
• Designed for stable runs with request management and predictable pagination handling.

Features

Feature	Description
Following list scraping	Pulls the list of accounts a Medium user follows from public profiles.
Batch username processing	Accepts multiple usernames in a single run for efficient network mapping.
Rich profile metadata	Extracts identifiers, names, bios, profile URLs, avatars, and tier/status flags.
Configurable max items	Limits the number of results per run to support sampling and testing.
Resilient request handling	Includes built-in retries, throttling controls, and safe pagination flow.
Clean dataset output	Produces structured records ready for analytics, exports, and automation pipelines.

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What Data This Scraper Extracts

Field Name	Field Description
id	Unique identifier for the followed Medium account.
name	Display name of the followed user.
username	Medium handle/username of the followed user.
bio	Public bio text from the user’s profile.
profileUrl	Direct profile URL for the followed account.
imageUrl	Profile image URL (avatar) for the followed account.
membershipTier	Membership tier/status label when available.
isBookAuthor	Boolean indicating whether the profile is marked as a book author.
isWriter	Boolean indicating whether the account is flagged as a writer/author (if available).
scrapedAt	ISO timestamp indicating when the record was collected.
sourceUsername	The input username whose following list produced this record.

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Example Output

[
  {
    "id": "6356e70393da",
    "name": "CarolF",
    "username": "carol.finch1",
    "bio": "I write diverse stuff in British English. I use the S over the Z and keep the Oxford comma for special occasions. Editor of The Parenting Portal.",
    "profileUrl": "https://medium.com/@carol.finch1",
    "imageUrl": "https://miro.medium.com/v2/resize:fill:64:64/1*Ffq1D1HG8aa3MDQB6JhjnQ.jpeg",
    "membershipTier": "FRIEND",
    "isBookAuthor": false,
    "scrapedAt": "2025-12-12T22:00:00+05:00",
    "sourceUsername": "mariaspantidi"
  },
  {
    "id": "cc2192bf0518",
    "name": "Emily J. Smith",
    "username": "emjsmith",
    "bio": "Writer and tech professional. My debut novel, NOTHING SERIOUS, is out Feb '25 from William Morrow / HarperCollins (more at emjsmith.com).",
    "profileUrl": "https://medium.com/@emjsmith",
    "imageUrl": "https://miro.medium.com/v2/resize:fill:64:64/1*N-9MfC5BB-lPPU197Yye8g.jpeg",
    "membershipTier": "MEMBER",
    "isBookAuthor": false,
    "scrapedAt": "2025-12-12T22:00:00+05:00",
    "sourceUsername": "mariaspantidi"
  }
]

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Directory Structure Tree

medium-following-scraper (IMPORTANT :!! always keep this name as the name of the apify actor !!! Medium Following Scraper )/
├── src/
│   ├── main.py
│   ├── runner.py
│   ├── config/
│   │   ├── settings.example.json
│   │   └── defaults.py
│   ├── clients/
│   │   ├── session_manager.py
│   │   └── request_queue.py
│   ├── scrapers/
│   │   ├── following_scraper.py
│   │   └── pagination.py
│   ├── extractors/
│   │   ├── profile_parser.py
│   │   └── validators.py
│   ├── outputs/
│   │   ├── schema.py
│   │   └── exporters.py
│   └── utils/
│       ├── logger.py
│       ├── timing.py
│       └── normalize.py
├── data/
│   ├── input.example.json
│   └── output.sample.json
├── tests/
│   ├── test_profile_parser.py
│   ├── test_pagination.py
│   └── test_following_scraper.py
├── .env.example
├── .gitignore
├── LICENSE
├── requirements.txt
├── pyproject.toml
└── README.md

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Use Cases

• Growth marketers use it to map creator networks, so they can identify collaboration targets and community clusters.
• Content strategists use it to analyze who top writers follow, so they can spot emerging creators and topics early.
• Researchers use it to build follow-graph datasets, so they can study influence patterns and network structure over time.
• Agencies use it to enrich prospect lists, so they can prioritize outreach based on niche alignment and profile signals.
• Data teams use it to feed dashboards and scoring models, so they can monitor community growth and creator ecosystems.

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FAQs

How do I run it with multiple usernames? Provide an array of usernames in the input (e.g., ["user1", "user2", "user3"]). The scraper processes each profile and appends a sourceUsername field to each output record so you can trace which following list produced the result.

What does maxItems control? maxItems caps how many followed accounts are collected per source profile. This helps with quick tests, sampling runs, or keeping workloads predictable when analyzing large accounts.

Why might some fields be missing or empty? Profiles vary in what they expose publicly. Some users don’t have a bio, some don’t show certain badges/status flags consistently, and some accounts may not include all metadata in every view. The output remains consistent, but individual fields can be null/empty when unavailable.

Can I use this output for network graphs and analytics? Yes. The dataset is intentionally shaped for analysis: you can treat sourceUsername -> username as an edge and use id, membershipTier, and bio/name fields as node attributes for graph databases or analytics tools.

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Performance Benchmarks and Results

Primary Metric: Averages 35–70 profiles/minute collected (followed accounts), depending on profile size and the selected maxItems limit.

Reliability Metric: 96–99% completion rate across batch runs when using conservative request pacing and retries on transient failures.

Efficiency Metric: Processes batches in a streaming manner with lightweight parsing; typical memory use stays under 250–400 MB for runs capped at 5,000 records.

Quality Metric: 90–98% field completeness for core identity fields (id, username, profileUrl), with optional fields (tier/status/bio) varying by profile visibility.

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