# Wikipedia Email Scraper - Domain Filter & Deduplication (`insightflow/wikipedia-email-scraper-powerful-and-intelligent`) Actor

📖 Wikipedia Email Scraper — turn Wikipedia searches into a clean contributor and organisation email list. Filter by keyword, location and domain, merge alias duplicates and decode hidden addresses. 🔍 For research & PR outreach.

- **URL**: https://apify.com/insightflow/wikipedia-email-scraper-powerful-and-intelligent.md
- **Developed by:** [InsightFlow](https://apify.com/insightflow) (community)
- **Categories:**
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.99 / 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** helps you extract publicly available email addresses from Wikipedia pages using keywords and filters you choose. It’s built for Wikipedia scraping, Wikipedia data extraction, and contact discovery, making it a practical email scraper for lead generation, research, and structured data extraction at scale. 🚀

### What Does Wikipedia Email Scraper Do? 🤖

Wikipedia Email Scraper searches public Wikipedia content based on your keywords, optional location, and selected email-domain filters. During a run, it scrapes relevant pages, extracts matching emails, filters duplicates, and exports the results to your Apify dataset in a clean, structured format. You can control the total number of emails with `maxEmails`, which helps with pacing and budget control. This is a faster, more scalable alternative to manual wiki crawler workflows and repetitive text mining.

### What Can Wikipedia Email Scraper Extract? 📊

This actor captures a small but highly useful set of fields for contact scraping and lead generation. Each record includes the keyword that surfaced the result, the page title, a text description, the page URL, and the extracted email address. That makes it easy to review leads, analyze context, and export the results into your own workflow.

| Data Type | Field Name | Description |
|---|---|---|
| Discovery | `keyword` | The keyword that led to this result |
| Identity | `title` | The page title shown in the dataset |
| Context | `description` | Text description associated with the page result |
| Navigation | `url` | Direct link to the Wikipedia page |
| Contact | `email` | Extracted email address from publicly available data |

#### Key Features of Wikipedia Email Scraper ⚡

- ✅ **Keyword-Driven Extraction:** Uses your keywords to find relevant Wikipedia pages and collect matching contact data.
- 🌍 **Optional Location Filter:** Narrow results by location when you want more focused Wikipedia scraping.
- 📧 **Custom Domain Filtering:** Limit email extraction to selected domains like `@gmail.com` or `@yahoo.com`.
- 🔄 **Reliable Run Behavior:** Includes retries, fallbacks, and progress-saving logic for more resilient web scraping.
- 📊 **Structured Dataset Output:** Saves each result as a clean record for analysis, export, or CRM use.
- 💾 **Live Saving:** Results are pushed during the run, so you do not lose progress if the run stops early.
- ⚙️ **Configurable Volume:** Set `maxEmails` to cap how many emails are collected per run.

### How to Use Wikipedia Email Scraper 🚀

1. **Open the Actor** — Find Wikipedia Email Scraper in the Apify Store.
2. **Add Keywords** — Enter one or more keywords such as job titles or roles.
3. **Set Optional Filters** — Use location and custom email domains to refine results.
4. **Choose a Limit** — Set `maxEmails` to control how many emails the actor collects.
5. **Run the Actor** — Start the run and monitor progress in the logs.
6. **Review Results** — Open the Dataset tab to inspect and export your scraped leads.

No coding required. ✨

### Wikipedia Email Scraper Output Format 📦

The actor saves results in the dataset using the exact fields shown below. This makes it easy to export the data for analysis, lead generation, or further structured data extraction.

#### Input Example

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

| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| `keywords` | Array | ✅ Yes | `["manager","founder"]` | One or more keywords or queries used to find relevant Wikipedia pages. |
| `location` | String | No | `""` | Optional location filter to narrow down search results. |
| `customDomains` | Array | No | `["@gmail.com","@yahoo.com"]` | Email domains to include when extracting contacts. |
| `maxEmails` | Integer | No | `20` | Maximum number of emails to collect before the actor stops. |

#### Output Example

```json
[
  {
    "keyword": "manager",
    "title": "Jane Smith",
    "description": "Marketing manager and public speaker based in London.",
    "url": "https://en.wikipedia.org/wiki/Jane_Smith",
    "email": "jane.smith@gmail.com"
  }
]
```

| Field | Label | Format | Description |
|---|---|---|---|
| `keyword` | Keyword | text | The keyword used to find the result. |
| `title` | Title | text | The page title returned in the dataset. |
| `description` | Description | text | Text context associated with the result. |
| `url` | Url | link | Direct link to the Wikipedia page. |
| `email` | Email | text | Extracted email address found in publicly available data. |

### 🎯 Use Cases of Wikipedia Email Scraper

**Lead Generation:** Build targeted lists of publicly available contacts for outreach campaigns, prospecting, and contact discovery.

**Email Marketing Campaigns:** Collect email addresses from relevant Wikipedia pages to support newsletters, follow-ups, and promotional workflows.

**Research and Entity Extraction:** Use Wikipedia scraping for Wikipedia parser-style analysis, text mining, and structured data extraction across topics.

**Market Research:** Gather page titles, descriptions, and contact details to understand niches, industries, and public-facing entities.

**CRM Enrichment:** Add extracted email data to existing records to improve segmentation and improve contact completeness.

### How Much Will Wikipedia Email Scraper Cost You? 💰

This actor is designed for controlled, practical runs with a configurable `maxEmails` limit, so you can manage output volume and keep costs predictable. Apify pricing depends on your platform usage, and setting a lower limit helps you keep each run focused. For larger campaigns, increase `maxEmails` carefully and review results in the Dataset tab as they arrive. 📈

### Is It Legal to Scrape Wikipedia? ⚖️

Wikipedia Email Scraper only accesses publicly available data on Wikipedia. It does not require login access or private content, and it is intended for legitimate research, marketing, and contact discovery use cases. As with any web scraping or email harvesting workflow, you are responsible for complying with applicable laws, data-protection rules, and Wikipedia’s terms. If you have questions, reach out at <insightflowofficial@gmail.com>.

### Wikipedia Email Scraper Input Parameters 📋

```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. |
| `maxEmails` | Integer | No | `20` | Maximum number of emails to collect. The actor stops once this limit is reached. |

### During the Actor Run ⏱️

You’ll see live logs in the Apify Console as the actor progresses through your keywords and email-domain filters. Results are saved in real time to the dataset, so you can inspect leads before the run finishes. Runtime depends on your input size, especially `maxEmails`, keyword breadth, and how much publicly available data matches your filters. If results are limited, try broader keywords or additional domains.

### Final Note ✉️

Start extracting Wikipedia emails in minutes with a simple, scalable Wikipedia Email Scraper. It’s an efficient way to automate Wikipedia data extraction and contact scraping with clean results you can use right away. Questions? Contact <insightflowofficial@gmail.com>.

### FAQ — Wikipedia Email Scraper ❓

#### How does the Wikipedia Email Scraper find emails?

It uses your keywords and email-domain filters to locate relevant Wikipedia pages, then extracts publicly available email addresses from the page content. The actor returns only matched contact data that is visible on the web.

#### What types of Wikipedia pages can I scrape?

You can scrape public Wikipedia pages that contain text and visible contact details matching your chosen keywords. If a page does not contain a relevant email address, it will not produce a contact result.

#### Why use Wikipedia scraping for lead generation?

Wikipedia scraping can support lead generation, research, and contact discovery when you want structured data from public web pages. It saves time by replacing manual review with automated extraction.

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

The actor supports controlled runs through the `maxEmails` parameter, so you can decide how much data to collect per run. That makes it easier to manage usage and keep scraping focused.

#### What are the main limitations of the Wikipedia Email Scraper?

Results depend on what publicly available data exists on the page. If a page has no matching email address, no result will be saved. Larger searches can also take longer, especially with broader keywords.

#### How do I choose the right keywords?

Use targeted keywords related to the type of contacts you want, then expand with similar terms if results are too limited. This usually improves email extraction, entity extraction, and overall lead quality.

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

It turns manual Wikipedia data extraction into an automated workflow, helping you collect leads faster, enrich records, and export structured data for campaigns or analysis.

#### Who do I contact for support or feedback?

For support, feedback, or custom feature requests, contact <insightflowofficial@gmail.com>.

### 🆘 Support & Feedback

Found a bug or need help with Wikipedia Email Scraper?

- 🐞 **Bug reports:** Share what happened and which input you used
- ✨ **Feature requests:** Send your ideas for new filters or output improvements
- 📧 **Email:** <insightflowofficial@gmail.com>

### MX Lookup

Every address is checked at the DNS level: the actor resolves the mail domain's
MX records and reports what it found.

| Field | Meaning |
| --- | --- |
| `mxFound` | `true` when the domain publishes at least one mail server |
| `mxHost` | The lowest-preference (primary) mail server |
| `mxRecords` | Every MX record found, in preference order |
| `mxProvider` | Who runs the mail: Google Workspace, Microsoft 365, Zoho, Proton, ... |
| `mxStatus` | `found`, `no_records`, `no_such_domain`, `timeout`, `error`, or `skipped` |

**Inputs**

- **MX Lookup** - turn the check on or off (default: on).
- **Only keep contacts whose domain has an MX record** - drop unreachable domains.
  Only a definite negative (`no_records` / `no_such_domain`) drops a contact; a
  timeout or resolver error is treated as unknown and the lead is kept.
- **MX lookup timeout (seconds)** - per-domain DNS budget, 1-15s.

This is a domain-level check. It confirms the domain can receive mail; it does
not verify that an individual mailbox exists.

# Actor input Schema

## `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

## `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.

## `minLeadScore` (type: `integer`):

Drop any contact whose Lead Score (0-100) falls below this. Leave at 0 for no filter.

## `mxLookup` (type: `boolean`):

Resolve each address's domain to its mail servers. Adds the resolved MX hosts and the mail provider (Google Workspace, Microsoft 365, ...) to every result, and feeds the Lead Score. DNS-level only - it confirms the domain can receive mail, not that the individual mailbox exists.

## `requireMxRecord` (type: `boolean`):

Drop any contact whose domain provably accepts no mail. A lookup that times out or errors is treated as unknown and kept, so a DNS hiccup never silently deletes good leads.

## `mxTimeoutSecs` (type: `integer`):

How long to wait for each DNS answer. Clamped to 1-15 seconds.

## Actor input object example

```json
{
  "keywords": [
    "manager",
    "founder"
  ],
  "location": "",
  "customDomains": [
    "@gmail.com",
    "@yahoo.com"
  ],
  "maxEmails": 20,
  "minLeadScore": 0,
  "mxLookup": true,
  "requireMxRecord": false,
  "mxTimeoutSecs": 3
}
```

# 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"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("insightflow/wikipedia-email-scraper-powerful-and-intelligent").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",
    ],
}

# Run the Actor and wait for it to finish
run = client.actor("insightflow/wikipedia-email-scraper-powerful-and-intelligent").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"
  ]
}' |
apify call insightflow/wikipedia-email-scraper-powerful-and-intelligent --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,insightflow/wikipedia-email-scraper-powerful-and-intelligent"
        }
    }
}

```

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/be6zBcNOqcdMkXRQn/builds/dB8HKfnVp1yrgzenQ/openapi.json