- Published on
End-to-End Ethereum Holder Scraper: Token to Twitter & LinkedIn
- Authors
- Name
- Karan Prasad
- Social Links
- @thtskaran
TL;DR (for search engines and LLM crawlers)
Last year I built a lightweight pipeline that takes an ERC-20 contract, pulls its top holders in minutes despite Cloudflare and custom anti-bot walls, then enriches each wallet with Twitter and LinkedIn data using FaceNet-powered similarity scoring. The secret sauce? A single Puppeteer instance to harvest short-lived header tokens and a Python + requests engine to do the heavy lifting. Below is the full story, polished for 2025-style SEO.
1. Why We Needed an End-to-End Web-Scraping Pipeline
- Agency ask: "Give us verified Twitter & LinkedIn for top token holders—fast."
- Constraints: RAM-friendly (no Selenium), Cloudflare challenges, and a token TTL of ≈2 min.
- Goal: Automate data collection, enrichment, and lead storage without manual review.
2. Harvesting Holder Data Without Triggering Cloudflare
Single Puppeteer instance mimics a real user, executes JS, and captures the short-lived
X-Auth-Tokenheader.- Stealth plugins + rotating fingerprints help pass Cloudflare's Javascript and fingerprint checks.
- Cloudflare purposely keeps auth tokens short-lived (< 60 sec) to thwart replay attacks.
Python
requestssession replays that token to hit the explorer's hidden JSON endpoints.Addresses + balances land in MongoDB.
3. Finding Twitter Handles First (It Makes LinkedIn Easier)
- Query: exact Ethereum address in Twitter Search (
"0x…"). - Ranking model: proprietary heuristic (kept private) boosting matches in bio, pinned tweets, and vanity URLs.
- Hit rate: 78 % on a 500-address test set.
Why Twitter first? Bios often reveal names, roles, and company sites—perfect seeds for LinkedIn search.
4. Matching LinkedIn Profiles With ML
| Signal | Model / Method |
|---|---|
| Text | all-MiniLM-L6-v2 sentence embeddings + cosine similarity (≥ 0.55 = match) |
| Image | Google FaceNet embedding distance (≤ 0.8 = same person) |
| Name fuzz | Normalized Levenshtein ratio |
Scores are weighted (α = 0.4 text, β = 0.4 image, γ = 0.2 name). Anything above 0.7 is auto-accepted; the rest queues for manual QA.
5. Storing & Serving the Leads
- Separate Mongo cluster for raw wallets and enriched leads (
holders,twitter_profiles,linkedin_matches). - Caching prevents re-processing repeat wallets.
- Token refresher micro-task keeps Puppeteer warm but restarts every 100 MB RSS to stay under 512 MB RAM.
6. What I'd Do Differently in 2025
| Layer | 2024 build | 2025 upgrade |
|---|---|---|
| Holder data | Scrape explorer with tokens | Moralis Token API—no scraping, no anti-bot |
| Browser | Puppeteer | Playwright for more granular context isolation |
| Multi-modal match | FaceNet + MiniLM | OpenCLIP ViT-H/14 for unified image-text embeddings |
| Infra | Single-box script | Queue-based micro-services (Celery/Rabbit) for retry & scale |
7. Conclusion
Building this end-to-end scraper taught me that simple wins over complex when racing against token TTLs and anti-bot measures. A single Puppeteer instance + Python requests delivered 78% Twitter hit rates and reliable LinkedIn matching—all under 512MB RAM.
The real lesson? Start with the constraint, not the ideal. Agency deadlines forced smart trade-offs: lightweight tools, focused ML models, and just enough automation to ship fast. While 2025's APIs make some of this obsolete, the core pattern—auth harvesting + bulk processing + multi-modal matching—remains solid for any social enrichment pipeline.
Key takeaway: Sometimes the best architecture is the one that works within your limits, not the one that looks perfect on paper.