# College S Email Scraper (`solid-scraper/college-s-email-scraper`) Actor 📧 College S Email Scraper extracts verified college student/prospect emails from College sites fast—ideal for outreach, lead gen, and marketing teams. 🇺🇸⚡ Target, capture, and grow your list with confidence. - **URL**: https://apify.com/solid-scraper/college-s-email-scraper.md - **Developed by:** [SolidScraper](https://apify.com/solid-scraper) (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 $2.99 / 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/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use 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 ### College S Email Scraper 📬 **College S Email Scraper** is a lead-generation tool that searches “College S” business listings for universities, departments, and campus-related businesses, then extracts contact information—especially email addresses—along with supporting details like phone numbers and social media profiles. Whether you’re a marketer, a researcher, or a data analyst, this college email scraper helps solve the core problem of turning campus contact pages and publicly available information into usable outreach-ready datasets—saving you hours of manual work. --- ### Why choose College S Email Scraper? | Feature | Benefit | | --- | --- | | ✅ **College email scraper workflow (business + website contact extraction)** | Extracts emails, phone numbers, and social media profiles from business websites in one run | | ✅ **Built-in proxy support for reliable scraping** | Helps reduce rate-limit and IP-block issues when scaling bulk email scraping for colleges | | ✅ **Reliability-oriented scraping flow** | Includes resiliency mechanisms such as fallbacks so you can keep runs moving at scale | | ✅ **Structured, dataset-ready output** | Saves consistent fields like full address, phone, and scraped contact lists for easy import | | ✅ **Scale-oriented limits & stop conditions** | Targets a max number of businesses and stops when quotas are reached | | ✅ **Apify-ready dataset saving** | Pushes results during execution so you don’t lose progress if something interrupts | --- ### Key features - 🔎 **College contact email extraction:** Finds businesses based on your search term and extracts emails from their websites (a college leads email scraper workflow). - 🌐 **Website-driven email discovery:** Collects scraped contact info using each business’s website as the primary source. - 📞 **Phone numbers + social profiles included:** Captures `scraped_phones` and `scraped_social_media` alongside emails to improve lead coverage. - 🛡️ **Proxy configuration support:** Works with proxy settings you provide (recommended for large-scale scraping). - ⏱️ **Controlled scaling:** Uses your `maxBusinesses` and location settings to limit results and stop once the target is met. - 💾 **Real-time dataset saving:** Pushes flattened rows to the Apify dataset as emails are found (progress is captured continuously). - ✅ **Resilience for incomplete data:** If a business has no website, it’s handled gracefully and marked with a clear scrape status in the output. --- ### Input Provide input via an `input.json` file. Example structure: ```json { "googleMapsSearchTerm": "College S", "googleMapsLocation": ["New York"], "maxBusinesses": 5, "scrapeMaxBusinessesPerLocation": false, "proxyConfiguration": { "useApifyProxy": true } } ```` #### Input Fields | Field | Required | Description | | --- | --- | --- | | `googleMapsSearchTerm` | Yes | Enter the business type or niche for email scraper (example given: `'coffee shops'`, `'dentists'`). In this actor UI it’s prefilled with `College S`. | | `googleMapsLocation` | Yes | Target geographic location(s) to search. Provide a list like `["Miami, Florida"]` or a single location string (schema expects an array). | | `maxBusinesses` | No | Maximum businesses with emails to find (range `1-1000`). The scraper stops when this target is reached. | | `scrapeMaxBusinessesPerLocation` | No | If enabled, the scraper collects up to `maxBusinesses` results per location. If disabled, it combines all locations up to a single total limit. | | `proxyConfiguration` | No | Proxy settings for scraping. Proxy support is recommended for larger runs. When present, the input includes `proxy support` (prefilled to `true`). | *** ### Output The actor saves your results to the **Business Contact Data** dataset in a flattened JSON structure (including one row per extracted email when emails are found). Example output row: ```json { "street_address": "123 Example St", "city": "New York", "zip": "10001", "state": "NY", "country_code": "US", "full_address": "123 Example St New York NY 10001 US", "website": "https://example.edu", "avg_rating": 4.3, "total_reviews": 120, "name": "Example University Department", "place_id": "ChIJN1t_tDeuEmsRUsoyG83frY4", "phone": "+1 555-123-4567", "lat": 40.7128, "long": -74.006, "scraped_phones": ["+1 555-111-2222"], "scraped_social_media": ["https://www.linkedin.com/company/example"], "emails_found": 2, "pages_scraped": 5, "scrape_status": "success", "email_found": "contact@example.edu" } ``` #### Output Fields | Field | Type | Description | | --- | --- | --- | | `name` | string | Business name | | `website` | string | Business website | | `phone` | string | Business phone number (from listing data) | | `full_address` | string | Full concatenated address string | | `city` | string | City | | `state` | string | State | | `zip` | string | Zip/postal code | | `country_code` | string | Country code | | `scraped_emails` | array | Emails found (list). Note: when the actor pushes flattened rows per email, it deletes this field in the flattened copy. | | `scraped_phones` | array | Phone numbers extracted from the business website | | `scraped_social_media` | array | Social media profile links extracted from the business website | | `emails_found` | number | Count of emails found for the business | | `pages_scraped` | number | Number of processed URLs/pages during website scraping | | `avg_rating` | number | Average rating from the listing data | | `total_reviews` | number | Total reviews from the listing data | | `scrape_status` | string | Scrape status (e.g. `success`, `failed`, `no_website`, `error`) | **Export note:** The dataset can be exported by Apify in common formats like JSON/CSV from the Apify Console (exact export formats depend on your workspace settings). *** ### How to use College S Email Scraper (via Apify Console) 1. **Open Apify Console**: Sign in at https://console.apify.com and open the **Actors** tab. 2. **Find the actor**: Search for **College S Email Scraper** and open its actor page. 3. **Configure input**: In the **INPUT** section, set: **`googleMapsSearchTerm`** to your niche keyword (prefilled with `College S`), and **`googleMapsLocation`** to one or more target locations. 4. **Set limits (optional but recommended)**: Adjust **Maximum Businesses With Emails (`maxBusinesses`)**. If you have multiple locations, decide whether to use **Scrape Max Businesses Per Location (`scrapeMaxBusinessesPerLocation`)**. 5. **Choose proxy settings (optional)**: In **Proxy Configuration**, enable and configure proxy settings as needed (the form prefill uses `proxy support: true`). 6. **Run the actor**: Click **Run**. Watch logs for progress and see results being saved as the run proceeds. 7. **Review results**: After completion, open the **Business Contact Data** dataset and export/filter the scraped leads. No coding required — get college contact email data in minutes with this education email harvesting software tool. *** ### Advanced features & SEO optimization - 🚦 **Email-only friendly behavior:** When email extraction can’t happen (e.g., no website found), the actor can mark records with `scrape_status` such as `no_website`, while still keeping dataset structure consistent for downstream processing—useful for a student email finder tool workflow. - 🧰 **Targeted “email extraction” pipeline:** Designed as a college contact email extractor that couples listing-level business info with website-level contact discovery, improving the odds of finding working emails. - 🔁 **Scalable execution controls:** The actor uses `maxBusinesses` and location handling to keep bulk email scraping for colleges bounded and predictable. - 📄 **Consistent structured output:** Output fields like `full_address`, `avg_rating`, `total_reviews`, plus scraped contact lists make it easy to build a college contact email database for campaigns and research. *** ### Best use cases - 🎯 **Admissions teams building outreach lists:** Compile department email lists for college admissions email outreach and event promotion. - 📞 **University marketing teams sourcing contact leads:** Export verified-feeling outreach data with phone and social media enrichment alongside emails. - 🧑‍🔬 **Researchers analyzing campus contact patterns:** Study how many departments have publicly listed contact emails across regions. - 🗂️ **Sales ops teams automating lead enrichment:** Pipe dataset rows into CRM workflows for faster follow-up and segmentation. - 🧾 **Data analysts validating contact coverage:** Compare `emails_found`, `pages_scraped`, and `scrape_status` to measure extraction yield per location. - 💼 **Partnership managers targeting campus departments:** Build a structured contact email database for co-marketing or sponsorship proposals. - 💻 **Developers integrating into pipelines:** Use the Apify dataset output fields to automate outreach list generation and quality checks. *** ### Technical specifications - **Supported Input Formats** - ✅ `googleMapsLocation` as an array of locations - ✅ `googleMapsSearchTerm` as a string - ✅ Optional limits: `maxBusinesses`, `scrapeMaxBusinessesPerLocation` - ✅ Optional proxy input via `proxyConfiguration` - **Proxy Support** - ✅ Proxy configuration via `proxyConfiguration` (example uses `proxy support`) - **Retry Mechanism** - ✅ Resilience is built into the scraping approach (exact retry counts and delays are handled internally) - **Dataset Structure** - ✅ Stored in the Apify dataset: **Business Contact Data** - ✅ Flattened rows are pushed during execution (one row per extracted email when emails are found) - **Rate Limits & Performance** - ✅ High-level throttling and controlled concurrency are used to support stable runs - **Limitations** - ❌ If a business has no website, emails cannot be extracted (records are marked with `scrape_status` like `no_website`) - ❌ Results depend on the availability of publicly accessible contact information on the websites discovered during the run *** ### FAQ #### Does College S Email Scraper extract emails only from college websites? ✅ Yes. The actor extracts contact information from business websites it discovers and then saves emails (along with phones and social links when available) into the dataset structure. #### What does `scrapeMaxBusinessesPerLocation` change? ✅ If `scrapeMaxBusinessesPerLocation` is enabled, the actor targets up to `maxBusinesses` results per location. If disabled, it combines all locations into a single total cap based on `maxBusinesses`. #### Will it return results when no emails are found? ✅ The actor handles “no website” and failure scenarios using `scrape_status`. It also tracks `emails_found` and `pages_scraped` so you can filter low-yield entries in your analysis. #### Can I use proxy settings for large runs? ✅ Yes. Use the `proxyConfiguration` input to configure proxy support. This is recommended for bulk email scraping for colleges and other large-scale tasks. #### How is the output saved? ✅ Results are pushed to the Apify dataset during execution. When emails are found, the actor pushes flattened rows per extracted email (so each dataset row includes `email_found` and business context). #### Can I validate emails? ✅ The actor has an internal `validate_emails` configuration controlled by the input value `validateEmails` in code (though it is not exposed in the provided input schema). If you set validation in your run environment, it will influence email validation behavior. #### Do I need to log in to use Apify Console? ✅ Yes. You need access to Apify Console to run actors and view the resulting dataset in the Output tab. #### Is it legal to use the scraped emails for outreach? ✅ You’re responsible for compliance. The actor collects information from publicly accessible sources, but you must follow applicable privacy laws (including GDPR/CCPA where relevant), spam regulations, and the terms of service of the sources. *** ### Support & feature requests Have an idea to improve College S Email Scraper? We’d love your feedback. 💡 - 💡 **Feature Requests:** For example, CSV-first exports, additional output columns for better CRM mapping, or more granular filters for college contact leads email scraper workflows. - 📧 **Contact:** Email us at . Your feedback helps shape the roadmap for this college email scraper. *** ### Closing CTA / Final thoughts *If you’re looking for a fast, SEO-friendly way to build a college leads email scraper dataset, **College S Email Scraper** is ready for scale.*\ *Run it once, export structured leads, and save hours of manual collection.* *** ### Disclaimer **This tool only accesses publicly accessible sources.** It does not access private profiles, authenticated data, or password-protected pages. You are responsible for complying with applicable laws (including GDPR/CCPA where relevant), spam regulations, and the terms of service of each website. For data removal requests, contact . Use this tool responsibly, ethically, and for legitimate purposes only. # Actor input Schema ## `googleMapsSearchTerm` (type: `string`): Enter the business type or niche for email scraper (e.g., 'coffee shops', 'dentists'). ## `googleMapsLocation` (type: `array`): Target geographic location for the email scraper (e.g., 'Miami, Florida'). ## `maxBusinesses` (type: `integer`): Target number of businesses to find (1-1000). The scraper will stop when this target is reached. ## `scrapeMaxBusinessesPerLocation` (type: `boolean`): If enabled, the scraper will collect up to `maxBusinesses` results per location. If disabled, it combines all locations up to a single total limit. ## `proxyConfiguration` (type: `object`): Proxy settings for scraping. Recommended for large-scale scraping. ## Actor input object example ```json { "googleMapsSearchTerm": "College S", "googleMapsLocation": [ "New York" ], "maxBusinesses": 5, "scrapeMaxBusinessesPerLocation": false, "proxyConfiguration": { "useApifyProxy": true } } ``` # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "googleMapsSearchTerm": "College S", "googleMapsLocation": [ "New York" ], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("solid-scraper/college-s-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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "googleMapsSearchTerm": "College S", "googleMapsLocation": ["New York"], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("solid-scraper/college-s-email-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "googleMapsSearchTerm": "College S", "googleMapsLocation": [ "New York" ], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": true } }' | apify call solid-scraper/college-s-email-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=solid-scraper/college-s-email-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "College S Email Scraper", "description": "📧 College S Email Scraper extracts verified college student/prospect emails from College sites fast—ideal for outreach, lead gen, and marketing teams. 🇺🇸⚡ Target, capture, and grow your list with confidence.", "version": "1.0", "x-build-id": "Tn3UgZSNX7eXF8jVI" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/solid-scraper~college-s-email-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-solid-scraper-college-s-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/solid-scraper~college-s-email-scraper/runs": { "post": { "operationId": "runs-sync-solid-scraper-college-s-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/solid-scraper~college-s-email-scraper/run-sync": { "post": { "operationId": "run-sync-solid-scraper-college-s-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "googleMapsLocation", "googleMapsSearchTerm" ], "properties": { "googleMapsSearchTerm": { "title": "Search Term", "type": "string", "description": "Enter the business type or niche for email scraper (e.g., 'coffee shops', 'dentists').", "default": "College S" }, "googleMapsLocation": { "title": "Location", "type": "array", "description": "Target geographic location for the email scraper (e.g., 'Miami, Florida').", "default": [ "New York" ], "items": { "type": "string" } }, "maxBusinesses": { "title": "Maximum Businesses With Emails", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Target number of businesses to find (1-1000). The scraper will stop when this target is reached.", "default": 5 }, "scrapeMaxBusinessesPerLocation": { "title": "Scrape Max Businesses Per Location", "type": "boolean", "description": "If enabled, the scraper will collect up to `maxBusinesses` results per location. If disabled, it combines all locations up to a single total limit.", "default": false }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy settings for scraping. Recommended for large-scale scraping." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```