# Wikipedia Email Scraper (`email_scraper/wikipedia-email-scraper`) Actor

Wikipedia Email Scraper collects editor and contributor contact emails from Wikipedia talk pages by keyword, useful for research and community outreach.

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

## Pricing

from $1.49 / 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.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

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

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

### 🔍 Wikipedia Email Scraper

**Wikipedia Email Scraper** is an Apify actor built for one job: turning plain keywords into a clean list of public email addresses published on Wikipedia (wikipedia.org). Instead of manually searching Google for "editor site:wikipedia.org" over and over, the wikipedia email scraper automates the entire search-and-extract workflow for you, page after page, keyword after keyword.

This wikipedia email scraper is built for marketers, recruiters, sales teams, and growth hackers who need real, publicly available Wikipedia contact emails for outreach — without touching Wikipedia's own interface or breaking any login walls. Every email the wikipedia email scraper returns comes from an indexed, publicly visible page that Google itself has already crawled.

If you've ever wondered how to find Wikipedia email addresses at scale, this wikipedia email scraper is the easiest way: give it a few keywords, pick the email domains you care about (Gmail, Yahoo, Outlook, or your own company domain), and let the wikipedia email scraper do the searching.

### 🔹 Wikipedia Email Scraper – Overview

At its core, the wikipedia email scraper runs Google searches scoped to wikipedia.org using the `site:` search operator, combined with your chosen keywords and email domain suffixes. It then scans each search result snippet for a matching email pattern and keeps only unique, newly discovered addresses.

Every result the wikipedia email scraper pushes to your dataset is tied back to the exact Wikipedia page (or the closest resolvable link) where the email was found, along with the keyword that surfaced it and the snippet text surrounding it — so you always know the context behind every Wikipedia lead.

Because the wikipedia email scraper is keyword-driven rather than crawling the whole site, you stay in control of scope: narrow wikipedia email scraper runs to a specific niche, job title, or product category, or run broad wikipedia email scraper searches across many keyword and domain combinations in a single job.

### 🔹 Key Features

> **Key Features of this Wikipedia Email Scraper**
>
> - 🔎 Keyword-driven search scoped to wikipedia.org using Google's `site:` operator
> - 📧 Automatic email-pattern extraction from search result snippets
> - 🌍 Optional location/region filter to localize wikipedia email scraper results
> - 📬 Custom email domain suffix filtering (Gmail, Yahoo, Outlook, or your own domain)
> - 🚫 Exclude-words filter to automatically skip unwanted snippets (e.g. competitor names)
> - 🔁 Automatic de-duplication so the same email is never pushed twice in one run
> - ⏸️ Resumable runs — the wikipedia email scraper remembers progress and continues where it left off
> - 📊 Incremental dataset pushes, so results are visible while the run is still in progress
> - ⚙️ Per keyword + domain result cap to control run size and cost precisely

### 🔍 Wikipedia Email Scraper – What Data You Can Extract

The wikipedia email scraper is purpose-built for one data type: public email addresses connected to Wikipedia pages, plus the context needed to qualify each lead. This keeps the wikipedia email scraper focused, fast, and genuinely useful for outreach rather than returning noisy, unrelated data.

> **Output / Data Fields**
> | Field | Description |
> |---|---|
> | `keyword` | The search keyword that produced this Wikipedia result |
> | `title` | The page title of the matching search result |
> | `description` | The snippet text in which the email address was found |
> | `url` | The resolved link to the Wikipedia-related result page |
> | `email` | The extracted, deduplicated email address |

Every row pushed by the wikipedia email scraper includes the keyword and snippet alongside the Wikipedia email, so you can immediately judge relevance before adding a contact to your outreach list — no separate lookup required.

### 🔹 Wikipedia Email Scraper – How to Use It

Running the wikipedia email scraper does not require any coding. Set a few input fields on the Apify Store page, start the run, and the wikipedia email scraper handles pagination, retries, and deduplication automatically in the background.

#### 🔹 How It Works / Step-by-Step

1. **Enter keywords** — add one or more search terms relevant to the Wikipedia contacts you want (for example, "editor" or "researcher").
2. **Choose email domains** — pick the domain suffixes to match, such as `@gmail.com`, `@yahoo.com`, or a company domain.
3. **(Optional) Set a location** — narrow the wikipedia email scraper search to a country or city.
4. **(Optional) Add exclude words** — skip snippets containing specific words or phrases.
5. **Set a max emails value** — cap how many addresses each keyword + domain pair should collect.
6. **Start the run** — the wikipedia email scraper searches Google, extracts emails from Wikipedia-related results, and pushes each new, unique lead straight to your dataset.

Because results are pushed incrementally, you can open the dataset while the wikipedia email scraper run is still active and start working with the Wikipedia leads that have already been found.

#### 🔍 How to Scrape Wikipedia Emails by Keyword

To scrape Wikipedia emails for a specific niche, enter one or more targeted keywords instead of a single broad term. The wikipedia email scraper treats every keyword and domain suffix as its own search pair, so three keywords and two domains will run six independent wikipedia email scraper searches in one job.

Narrower keywords tend to return more relevant Wikipedia contacts, while broader keywords return more total volume at the cost of some noise. Most wikipedia email scraper users mix a few broad and a few specific keywords in the same run to balance coverage and relevance.

#### 🔍 Can You Filter Wikipedia Emails by Domain or Location?

Yes. The wikipedia email scraper supports both an email-domain filter and an optional location filter. The domain filter restricts results to the suffixes you list (for example only `@gmail.com` addresses), while the location filter narrows the underlying Google search to a specific country or city.

Both filters are optional. Leaving the location field empty makes the wikipedia email scraper search without any geographic restriction, which is useful when you want the widest possible pool of Wikipedia leads.

### 🔹 Wikipedia Email Scraper – Automation and Integration

Because the wikipedia email scraper is a standard Apify actor, it fits directly into existing automation stacks. You can start a run from the Apify API, trigger it from a webhook, or schedule it to repeat automatically, feeding fresh Wikipedia leads into a CRM, spreadsheet, or email tool over time.

> **Automation Highlights**
>
> - 🔗 Full Apify API support for starting and monitoring wikipedia email scraper runs programmatically
> - ⏰ Native Apify scheduling for recurring wikipedia email scraper jobs
> - 🔔 Apify webhooks to notify downstream systems the moment a run finishes
> - 📦 JSON, CSV, and Excel export for direct use in CRMs, spreadsheets, or mail-merge tools

### 🔍 Is the Wikipedia Email Scraper Legal and Accurate?

The wikipedia email scraper only collects email addresses that are already publicly visible in Google's search index for wikipedia.org — it does not log in to Wikipedia, bypass any paywall, or access private account data. It simply automates a search that anyone could already run manually.

That said, as with any public email scraper, you remain responsible for using the Wikipedia contacts you collect in compliance with applicable email marketing laws (such as CAN-SPAM or GDPR) in your own jurisdiction and the recipient's. The wikipedia email scraper surfaces public data; how you use it is up to you.

### 🔹 Wikipedia Email Scraper – Use Cases

> **Why Use This Actor**
>
> - 📈 **Sales prospecting** — build a list of Wikipedia contacts to pitch a product or service
> - 🧑‍💼 **Recruiting outreach** — find Wikipedia-connected professionals matching a role or skill
> - 🤝 **Partnership & influencer outreach** — identify Wikipedia accounts worth contacting for collaborations
> - 📣 **Marketing & PR campaigns** — reach Wikipedia-related audiences directly by email
> - 🧪 **Market research** — sample publicly listed Wikipedia contacts for a given niche or region

Any workflow that starts with "I need real email addresses connected to Wikipedia" is a good fit for this wikipedia email scraper. Because every result is grounded in a real, indexed search snippet, the leads it returns are usable for genuine outreach rather than guesswork.

### 🔹 Input

> **Input Fields**
> | Field | Type | Description |
> |---|---|---|
> | `keywords` | array | One or more search keywords or queries to run the search for |
> | `location` | string | Optional country or city to narrow the search geographically |
> | `customDomains` | array | Email domain suffixes to match, e.g. `@gmail.com`, `@yahoo.com` |
> | `maxEmails` | integer | Maximum emails to collect **per keyword + domain combination** |
> | `excludeWords` | array | Words/phrases that, if found in a snippet, cause it to be skipped |

The `maxEmails` field is a per-combination cap, not a single run-wide total. With 3 keywords and 2 domains, a `maxEmails` value of 20 lets the wikipedia email scraper collect up to 20 emails for *each* of the 6 keyword + domain pairs — up to 120 Wikipedia emails in total for that run.

### 🔹 Output

Results from the wikipedia email scraper are written to the Apify dataset as structured JSON rows, and can be exported directly from the Apify Store UI or API as JSON, CSV, or Excel. A sample row looks like this:

```json
{
  "keyword": "editor",
  "title": "Example Wikipedia result title",
  "description": "...snippet text containing the email...",
  "url": "https://wikipedia.org/example-page",
  "email": "example@gmail.com"
}
```

Because every wikipedia email scraper row is deduplicated against previously seen emails in the same run, your dataset never contains the same Wikipedia email twice, even across resumed runs.

### 🔹 Pricing

> **Pricing Notes**
>
> - Free-tier usage of this actor is capped at **100 emails per keyword + domain combination**.
> - Paid/upgraded usage removes this cap, limited only by the `maxEmails` value you set.
> - Results are billed per result pushed to your dataset, consistent with Apify's actor pricing model.

If a wikipedia email scraper run on the free tier stops at 100 results for a keyword + domain pair, this is the free-tier limit, not an error — upgrading removes the ceiling so the wikipedia email scraper can continue collecting up to your configured `maxEmails` value.

### 🔹 Tips and Best Practices

> **Tips / Important Notes**
>
> - Use 3–5 specific keywords rather than one generic term for better Wikipedia coverage.
> - Add multiple email domain suffixes (`@gmail.com`, `@yahoo.com`, `@outlook.com`) to widen results.
> - Leave the location field empty for maximum reach, or set it for geo-targeted leads.
> - Use the exclude-words filter to automatically skip irrelevant or unwanted snippets.
> - Sparse results usually mean the wikipedia email scraper search is too narrow — broaden keywords or domains.

The wikipedia email scraper naturally slows down and stops once Google stops returning new, relevant Wikipedia results for a given keyword and domain pair — this is expected behavior, not a bug. Broadening keywords or adding more domain suffixes is the most effective way to get more Wikipedia leads from the wikipedia email scraper.

### 🔹 Frequently Asked Questions

#### 🔍 What is the Wikipedia Email Scraper?

The Wikipedia Email Scraper is an Apify actor that searches Google for public email addresses connected to Wikipedia (wikipedia.org) pages, based on the keywords and email domains you provide, then returns them as a structured, de-duplicated dataset.

#### 🔍 How do I use the Wikipedia Email Scraper?

Enter one or more keywords, choose your target email domains, optionally set a location and exclude words, then start the run. The wikipedia email scraper pushes new Wikipedia leads to your dataset as it finds them.

#### 🔍 What data does the Wikipedia Email Scraper return?

Each result includes the keyword used, the result's title and description snippet, the resolved Wikipedia-related URL, and the extracted email address — giving you context for every lead the wikipedia email scraper finds.

#### 🔍 Is there a limit on how many Wikipedia emails I can collect?

Free-tier runs of the wikipedia email scraper are capped at 100 emails per keyword + domain combination. Paid usage follows the `maxEmails` value you configure, with no additional platform-side cap.

#### 🔍 Can I run the Wikipedia Email Scraper on a schedule?

Yes. Like any Apify actor, the wikipedia email scraper can be triggered manually, via the Apify API, or on a recurring schedule using Apify's built-in scheduler, so you can keep your Wikipedia lead list growing automatically over time.

#### 🔍 Does the Wikipedia Email Scraper work without coding knowledge?

Yes. The wikipedia email scraper is fully no-code: all configuration happens through simple input fields on the Apify Store page, and results are ready to download as JSON, CSV, or Excel once the run finishes.

### 🔹 Support

Questions about this wikipedia email scraper, feature requests, or a bespoke build for a different site? Reach out any time.

- contact me by email : alphascraper69@gmail.com

# Actor input Schema

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

A list of keywords or queries to search for.

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

Optional country, state or city used to narrow the search. Leave it empty to search without a geographic filter.

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

List of custom email domains

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

How many addresses each search keyword + domain suffix combination may collect before the finder moves on to the next one. This is a per-combination target, not a run-wide total: with 3 keyword and 2 Domains and a limit of 20, the run works through all 6 combinations and aims for up to 20 addresses in each, so up to 120 overall. Lower values finish sooner and cost less; higher values dig deeper but never guarantee a fuller result, since the run can only find what is publicly listed.

## `excludeWords` (type: `array`):

Words or phrases you do not want to see.

## Actor input object example

```json
{
  "keywords": [
    "editor",
    "researcher"
  ],
  "location": "",
  "customDomains": [
    "@gmail.com"
  ],
  "maxEmails": 5,
  "excludeWords": []
}
```

# 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": [
        "editor",
        "researcher"
    ],
    "location": "",
    "customDomains": [
        "@gmail.com"
    ],
    "excludeWords": []
};

// Run the Actor and wait for it to finish
const run = await client.actor("email_scraper/wikipedia-email-scraper").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": [
        "editor",
        "researcher",
    ],
    "location": "",
    "customDomains": ["@gmail.com"],
    "excludeWords": [],
}

# Run the Actor and wait for it to finish
run = client.actor("email_scraper/wikipedia-email-scraper").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": [
    "editor",
    "researcher"
  ],
  "location": "",
  "customDomains": [
    "@gmail.com"
  ],
  "excludeWords": []
}' |
apify call email_scraper/wikipedia-email-scraper --silent --output-dataset

```

## MCP server setup

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

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/TH6G3IyztZIyIkhsq/builds/aAQb8YE1uMpdTaEby/openapi.json