# Wikipedia Email Scraper - Bulk Keywords, Decodes Hidden Emails (`code-beat/wikipedia-email-scraper-ultra-fast-and-precise`) Actor

📖 Wikipedia Email Scraper collects contributor and referenced org emails from bulk keyword searches. 🔓 Decodes written-out addresses with domain filters. 🔍 Perfect for research teams, PR outreach & citation-based prospecting.

- **URL**: https://apify.com/code-beat/wikipedia-email-scraper-ultra-fast-and-precise.md
- **Developed by:** [Code Beat](https://apify.com/code-beat) (community)
- **Categories:** Lead generation, Automation, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.50 / 1,000 results

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

### Wikipedia Email Scraper

**Wikipedia Email Scraper** is a fast, practical way to perform Wikipedia email extraction from publicly available data. Built for marketers, data analysts, and researchers, this Wikipedia contact scraping tool helps you find relevant emails using keywords, optional location filtering, and domain preferences — so you can scale lead generation without hours of manual wiki parser work. 📬

### What is Wikipedia Email Scraper? 🔍

Wikipedia Email Scraper is an Apify actor that automates web scraping of publicly available Wikipedia pages to find email addresses related to the keywords you provide. It helps turn manual Wikipedia data extraction into a repeatable workflow for contact data scraping and email harvesting.

This Wikipedia page scraper is especially useful when you need to collect public contact details at scale without scanning pages one by one. Instead of spending hours reviewing pages manually, you can use this Wikipedia scraping tool to surface relevant results faster and organize them into a structured dataset. It’s a strong fit for marketers, recruiters, sales teams, and analysts who need entity extraction and information extraction from public web data. 🚀

### What Data Does Wikipedia Email Scraper Collect? 📊

This actor collects the fields needed for practical lead generation and contact discovery: the keyword that triggered the result, the page title, supporting description text, the page URL, and the extracted email address. Each result is stored in a clean dataset for easy export and downstream analysis.

| Data Category | Fields Extracted | Description |
|---|---|---|
| Discovery | `keyword` | The keyword that surfaced the result |
| Identity | `title` | Page title returned for the result |
| Context | `description` | Supporting text associated with the page |
| Navigation | `url` | Direct link to the page |
| Contact | `email` | Public email address found in the result |

### What Do Results from Wikipedia Email Scraper Look Like? 👀

Each result is saved as a structured JSON record in your Apify dataset. Here’s a realistic example of what Wikipedia data extraction can look like in practice:

```json
{
  "keyword": "founder",
  "title": "Dr. Emily Carter",
  "description": "Entrepreneur and keynote speaker focusing on nonprofit leadership and public health initiatives.",
  "url": "https://en.wikipedia.org/wiki/Emily_Carter",
  "email": "emily.carter@gmail.com"
}
```

You can export results in JSON from the Apify dataset and also use CSV export through Apify Console.

#### Core Features: Wikipedia Email Scraper ⚡

| Feature | Benefit |
|---|---|
| ✅ **Keyword-Driven Targeting** | Find public contacts that match your search terms more precisely |
| ✅ **Location Filter** | Narrow results with an optional location value |
| ✅ **Custom Domain Filter** | Focus on emails from domains such as `@gmail.com` or `@yahoo.com` |
| ✅ **Configurable Result Cap** | Use `maxEmails` to control how many results are collected |
| ✅ **Built-In Proxy Support** | Helps keep large runs more reliable on public web data |
| ✅ **Real-Time Data Saving** | Results are stored as they are found, reducing the risk of data loss |
| ✅ **Structured Dataset Output** | Clean records make export and analysis easier |
| ✅ **No Login Required** | Works on publicly available pages only |

### Getting Started with Wikipedia Email Scraper 🚀

1. **Open Apify** — Sign in to your Apify account and open the actor page for Wikipedia Email Scraper.
2. **Review the Input Form** — Start with the built-in input fields for keywords, location, custom email domains, and max results.
3. **Add Keywords** — Enter the topics, roles, or entities you want to search for.
4. **Set Optional Filters** — Add a location or restrict results to specific email domains if needed.
5. **Choose Your Result Cap** — Set `maxEmails` to control the run length and output volume.
6. **Run the Actor** — Launch the scraper and monitor progress in the log output.
7. **Open the Dataset** — Review the scraped leads in the dataset tab.
8. **Export Your Data** — Download the results in JSON or CSV for analysis, outreach, or CRM use.

No coding required — just configure, run, and collect public contacts at scale. ⚙️

### Ways to Use Wikipedia Email Scraper 💡

- 🎯 **Lead Generation** — Build targeted contact lists from Wikipedia pages using relevant keywords
- 📣 **Email Marketing** — Collect public email addresses for outreach campaigns and follow-ups
- 🔬 **Research Projects** — Gather Wikipedia data extraction results for academic or market research
- 🤝 **Recruitment** — Find public contact details for professionals, speakers, and experts
- 📊 **CRM Enrichment** — Add page titles, descriptions, and emails to existing records
- ⚙️ **Automated Email Collection** — Run recurring public web scraping workflows for fresh data

#### Input Parameters — Wikipedia Email Scraper

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

| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| `keywords` | Array | Yes | `["manager","founder"]` | A list of keywords or queries to search for. |
| `location` | String | No | `""` | Location to filter search results. |
| `customDomains` | Array | No | `["@gmail.com","@yahoo.com"]` | List of custom email domains to include in the search. |
| `maxEmails` | Integer | No | `20` | Maximum number of emails to collect. The scraper stops once this limit is reached. |

#### Output Parameters — Wikipedia Email Scraper

```json
{
  "keyword": "founder",
  "title": "Dr. Emily Carter",
  "description": "Entrepreneur and keynote speaker focusing on nonprofit leadership and public health initiatives.",
  "url": "https://en.wikipedia.org/wiki/Emily_Carter",
  "email": "emily.carter@gmail.com"
}
```

| Field | Label | Format | Description |
|---|---|---|---|
| `keyword` | Keyword | text | The keyword that produced the result. |
| `title` | Title | text | The page title returned in the dataset. |
| `description` | Description | text | Supporting text associated with the result. |
| `url` | Url | link | Direct link to the page. |
| `email` | Email | text | Public email address found in the result. |

### Why Choose Wikipedia Email Scraper? 🏆

Wikipedia Email Scraper gives you a reliable way to turn public web data into a usable contact list. It combines keyword targeting, domain filtering, structured output, and incremental saving, making it useful for contact data scraping workflows where speed and organization matter. Compared with manual research, this Wikipedia lead generation tool saves time and helps you work at scale. Built-in retry behavior and proxy support add extra resilience for larger runs. Need help? Reach out at <codebeatapi@gmail.com>. ✨

### How Many Results Can You Scrape? 📈

You control output volume with `maxEmails`, which supports values from 1 to 10,000. Actual results depend on how many public pages match your keywords and contain email addresses. Larger keyword sets and broader domain choices can increase yield, while the dataset can store all collected results for later export.

### Legal Guidelines for Scraping Wikipedia ⚖️

Wikipedia Email Scraper only accesses publicly available data. It does not log in to private accounts or access protected content. You are responsible for following applicable laws, including privacy and anti-spam rules, as well as any relevant website policies. Always use extracted public contact data responsibly and for legitimate purposes. For data removal requests, contact <codebeatapi@gmail.com>.

### FAQ — Wikipedia Email Scraper ❓

#### How does Wikipedia Email Scraper find email addresses?

It uses your keywords and email-domain filters to find relevant public pages, then extracts email addresses from publicly available sources and stores the matches in your dataset.

#### What kind of pages can I scrape with Wikipedia Email Scraper?

You can scrape public Wikipedia pages that include contact details in visible page content. If a page does not contain an email address, it simply won’t produce a contact result.

#### Why use Wikipedia Email Scraper for contact data scraping?

It automates manual Wikipedia scraping pages work and turns scattered public information into structured leads you can export, filter, and analyze more efficiently.

#### How much does Wikipedia Email Scraper cost?

Pricing depends on your Apify usage and run settings. You can use `maxEmails` to keep runs controlled and cost-efficient while still collecting useful results.

#### How does Wikipedia Email Scraper help my business?

It helps teams speed up email address extraction, build contact lists, enrich CRM records, and support research workflows without manual page-by-page review.

#### What should I expect when using Wikipedia Email Scraper?

Results depend on keyword quality, domain filters, and the amount of public contact information available. Broader keywords can improve coverage, while tighter filters can improve relevance.

#### How do I get better results from Wikipedia Email Scraper?

Use targeted keywords, consider adding related terms, include several relevant email domains, and set a realistic `maxEmails` value based on your lead generation goal.

#### Is Wikipedia Email Scraper suitable for large-scale scraping?

Yes, it is designed for scalable public web scraping with structured output, incremental saving, and built-in proxy support for more reliable runs.

### Conclusion 🏁

The Wikipedia Email Scraper is a practical way to extract public emails from Wikipedia pages at scale. Whether you’re building lead lists, researching entities, or enriching contact databases, it gives you a fast and organized workflow for public contact discovery. Start with your keywords and let the actor do the heavy lifting. 🚀

### 🆘 Support & Feedback

Have a question or feature request for Wikipedia Email Scraper?

For bug reports, custom solutions, or general feedback, please contact <codebeatapi@gmail.com>.

### Country & Time Targeting

Both filters are applied to the Google query itself, so they shape which pages
the dork is answered from rather than filtering after the fact.

**Target Country** - runs the search as if from that country (`gl`). Turn on
**Strict country filter** to additionally restrict results to pages Google
attributes to it (`cr=countryXX`); that is much tighter and returns noticeably
fewer results. Leave the country on *Global (no country filter)* for worldwide
results.

**Result Language** - restricts results to a single language (`hl` + `lr`).

**Time Range** - limits results to a publication window: past hour, 24 hours,
week, month, year, or an explicit *Custom range* using **Custom range: from** /
**to** in `YYYY-MM-DD` form. A page Google indexed last week is far more likely
to carry a live mailbox than one it last saw five years ago.

Selecting *Custom range* without either date falls back to no time filter rather
than searching all of time by accident.

# Actor input Schema

## `scrapeMode` (type: `string`):

Find New Emails (default) searches Airbnb by keyword. Clean My List skips searching entirely and instead runs the emails you paste into Email List (below) through the same deliverability checks.

## `keywords` (type: `array`):

A list of keywords or queries to search for.

## `location` (type: `string`):

Location to filter search results.

## `customDomains` (type: `array`):

List of custom email domains

## `emailList` (type: `array`):

Paste the emails you want checked for deliverability. Only used when Scrape Mode is set to "Clean My List".

## `maxEmails` (type: `integer`):

Maximum number of emails to collect. The scraper will stop once this limit is reached. Setting a higher limit allows for more potential results but doesn't guarantee reaching that number. This helps save costs by controlling scraping time.

## `verificationMode` (type: `string`):

Quick Check (default) validates syntax and MX records only - fast, no added latency. Deep Check additionally flags disposable-domain addresses and checks each domain against a public spam blocklist (Spamhaus DBL). Off skips validation entirely.

## `minQualityScore` (type: `number`):

Drop any contact whose quality score (0.0-1.0) falls below this. Leave at 0 for no filter.

## `excludeCatchAll` (type: `boolean`):

Drop contacts on domains confirmed to accept mail for any address (only applies when that signal is available).

## `requireMxValid` (type: `boolean`):

Drop any contact whose domain has no valid mail server (MX record).

## `targetCountry` (type: `string`):

Ask Google to answer as if searching from this country. Leave on "Global (no country filter)" for worldwide results.

## `strictCountry` (type: `boolean`):

Restrict results to pages Google attributes to the target country (cr=countryXX), instead of only preferring them. Much tighter targeting, noticeably fewer results.

## `searchLanguage` (type: `string`):

Restrict results to one language. Leave on "Any language" for no filter.

## `timeRange` (type: `string`):

Only return pages Google indexed within this window. Recent pages are more likely to hold a mailbox that still works.

## `customDateFrom` (type: `string`):

Only used when Time Range is "Custom range". Format: YYYY-MM-DD.

## `customDateTo` (type: `string`):

Only used when Time Range is "Custom range". Format: YYYY-MM-DD.

## Actor input object example

```json
{
  "scrapeMode": "Find New Emails",
  "keywords": [
    "manager",
    "founder"
  ],
  "location": "",
  "customDomains": [
    "@gmail.com",
    "@yahoo.com"
  ],
  "emailList": [],
  "maxEmails": 20,
  "verificationMode": "Quick Check",
  "minQualityScore": 0,
  "excludeCatchAll": false,
  "requireMxValid": false,
  "targetCountry": "Global (no country filter)",
  "strictCountry": false,
  "searchLanguage": "",
  "timeRange": "Any time",
  "customDateFrom": "",
  "customDateTo": ""
}
```

# Actor output Schema

## `dataset` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "keywords": [
        "manager",
        "founder"
    ],
    "location": "",
    "customDomains": [
        "@gmail.com",
        "@yahoo.com"
    ],
    "emailList": []
};

// Run the Actor and wait for it to finish
const run = await client.actor("code-beat/wikipedia-email-scraper-ultra-fast-and-precise").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "keywords": [
        "manager",
        "founder",
    ],
    "location": "",
    "customDomains": [
        "@gmail.com",
        "@yahoo.com",
    ],
    "emailList": [],
}

# Run the Actor and wait for it to finish
run = client.actor("code-beat/wikipedia-email-scraper-ultra-fast-and-precise").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "keywords": [
    "manager",
    "founder"
  ],
  "location": "",
  "customDomains": [
    "@gmail.com",
    "@yahoo.com"
  ],
  "emailList": []
}' |
apify call code-beat/wikipedia-email-scraper-ultra-fast-and-precise --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,code-beat/wikipedia-email-scraper-ultra-fast-and-precise"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/ZIzQ31fLk0T7zqIZV/builds/bxODzroE3zUJhGibL/openapi.json