Wikipedia Email Scraper - Keyword & Location Targeting

Wikipedia Email Scraper 🔍

Wikipedia Email Scraper helps marketers, recruiters, and sales pros quickly find relevant email addresses without manually hunting through pages one by one. It’s an email extraction tool designed to scrape Wikipedia for targeted Wikipedia email extractor results—perfect for extracting emails from Wikipedia and speeding up Wikipedia lead generation at scale and pace.

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🌟 Key Features of Wikipedia Email Scraper

Feature	Benefit
✅ Targeted Keyword Search	Reach the exact Wikipedia audience you need using your chosen terms
✅ Location Filtering	Narrow results to a specific area to improve relevance for email extraction
✅ Custom Domain Filter	Focus on specific email address detection rules like @gmail.com or @yahoo.com
✅ Bulk Export (JSON / CSV)	Drop results straight into your CRM or email tool for dataset building
✅ Proxy-Ready	Built for reliable web scraping with support for uninterrupted runs
✅ Real-Time Saving	Each result is saved incrementally to prevent data loss on longer scraping automations
✅ Resilient Execution	Includes retries and fallbacks to keep scraping stable when pages are slow or blocked
✅ Deduplicated Emails	Avoid repeated contacts so your contact information parsing stays clean

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📥 Input — Wikipedia Email Scraper Parameters

{
  "keywords": ["manager", "founder"],
  "location": "",
  "customDomains": ["@gmail.com", "@yahoo.com"],
  "maxEmails": 20
}

Parameter	Type	Required	Default	Description
keywords	Array	✅ Yes	—	Keywords or queries used to search for Wikipedia pages that match your topic for email extraction
location	String	No	""	Optional location filter to focus results on a specific city, region, or country
customDomains	Array	No	[]	Optional list of email domains (like @gmail.com, @yahoo.com) to filter email address detection
maxEmails	Integer	No	20	Upper cap on total emails to collect for this run (helps control scraping time and cost)

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📤 Output — What Wikipedia Email Scraper Returns

The actor saves each result as a JSON record in your Apify dataset.

{
  "keyword": "manager",
  "title": "Maria Jensen (Project Management)",
  "description": "Maria Jensen is a project manager with experience across construction and operations. Contact emails are listed in publicly available sources.",
  "url": "https://en.wikipedia.org/wiki/Maria_Jensen",
  "email": "maria.jensen@gmail.com"
}

Field	Type	Description
keyword	String	The search term that surfaced this Wikipedia result during scraping Wikipedia
title	String	Wikipedia profile name or business title associated with the extracted contact
description	String	Bio or profile summary text used for contact information parsing and email address detection
url	String	Direct link to the Wikipedia page that the email was extracted from
email	String	The extracted email address that matched your customDomains filters

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💻 How to Use Wikipedia Email Scraper — Step-by-Step

1. Open the Actor — Find Wikipedia Email Scraper on Apify Store
2. Enter Keywords — Add role titles, responsibilities, or business terms (e.g., “manager”, “founder”) for extracting emails from Wikipedia
3. Set Location (optional) — If you want geo-targeted Wikipedia lead generation, add a city or region
4. Filter by Domain (optional) — Limit results to specific domains like @gmail.com or @yahoo.com
5. Set Max Emails — Cap results to control dataset building cost and runtime
6. Run the Actor — Start the run and monitor progress in live logs
7. Export Results — Download your Wikipedia email list from the dataset tab (JSON / CSV)

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💡 Best Use Cases for Wikipedia Email Scraper

• 🎯 B2B Lead Generation — Build targeted email lists from Wikipedia for outbound sales campaigns
• 📣 Email Marketing — Use scraped Wikipedia contacts to power outreach and newsletter sequences
• 🤝 Recruitment — Find and contact relevant professionals using email address detection from publicly available sources
• 🔬 Market Research — Identify industry voices and niche communities for data mining workflows
• 📊 CRM Enrichment — Add emails to existing records after scraping Wikipedia data into a clean dataset

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Disclaimer

This actor only accesses publicly available data on Wikipedia. It does not scrape private profiles, authenticated content, or password-protected pages. You are responsible for ensuring your use complies with Wikipedia’s Terms of Service, GDPR, CCPA, and all applicable anti-spam laws. This tool is intended for legitimate purposes only, including lead generation, research, and marketing in compliance with local regulations. For data-removal requests, contact 📧 scrapidocontact@gmail.com.

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🆘 Support & Feedback

Have a question or found an issue with the Wikipedia Email Scraper? We're here to help.

• 🐞 Bug Reports: Open a ticket in the repository's Issues section
• ✨ Custom Solutions & Feature Requests: Reach out to our team
• 📧 Email: scrapidocontact@gmail.com

Your feedback shapes the roadmap — we read every message.

Multiple Email Types

Email Types replaces the old single Audience Type choice: select as many kinds of mailbox as you want and the run chases all of them together.

Type	What it matches
Personal / free webmail	Gmail, Outlook, Yahoo, iCloud, AOL, Proton, ...
Business / corporate	Company domains - free webmail and institutions excluded
Education (.edu / .ac)	.edu, .ac.uk, .edu.au, .ac.in and other academic suffixes
Government (.gov / .mil)	.gov, .mil, .gov.uk, .gc.ca, ...
Non-profit (.org)	.org, .ngo, .org.uk, ...

Each selected type contributes its own Google dork patterns and its own domain test, so a result is only kept if it genuinely belongs to the type that found it. Every row carries an emailType field recording which one that was.

Suffixes are matched as real domain suffixes, so cs.mit.edu counts as Education while notedu.com does not.

Setting Custom Email Domains still overrides everything: an explicit domain list is a manual override and replaces the type-driven patterns. The legacy audienceType value is still accepted, so saved inputs keep working.