# Quora Email Scraper (`scrapevanta/quora-email-scraper`) Actor Quora Email Scraper extracts publicly available email addresses, profile details, bios, follower counts, topics, social links, and activity data from Quora profiles. Ideal for lead generation, audience research, influencer outreach, networking, and market intelligence. - **URL**: https://apify.com/scrapevanta/quora-email-scraper.md - **Developed by:** [ScrapeVanta](https://apify.com/scrapevanta) (community) - **Categories:** Automation, Lead generation, Social media - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $5.99 / 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 ### Quora Email Scraper 🚀 Finding emails on Quora profiles (and then turning them into a usable outreach list) is slower than it should be. **Quora Email Scraper** helps you extract emails from Quora profiles using your chosen keywords and email-domain filters. It’s also a solid Quora scraper email and Quora contact email finder when you need scale. This Quora email extraction tool is built for outreach teams, lead generation analysts, and researchers who want real contacts fast. In one run, you can collect multiple email results based on your targeting—without manual profile-by-profile searching. --- ### See the Data: Sample Output Here's a real record from a single run: ```json { "network": "Quora.com", "keyword": "founder", "title": "Example profile post/title from search results", "description": "Example snippet text that may include an email address like name@gmail.com", "url": "https://www.quora.com/some-example-page", "email": "jane.doe@gmail.com", "proxyGroups": ["GOOGLE_SERP"], "status": "success" } ```` | Field | Type | What It Tells You | |---|---|---| | `network` | string | The source network the email was associated with (`Quora.com`). | | `keyword` | string | Which keyword drove the result (useful for auditing and repeating searches). | | `title` | string | The title extracted from the underlying result content (helps you judge relevance). | | `description` | string | The text snippet the email was found in (keeps context with the email). | | `url` | string | The page URL tied to the record, so you can verify source context. | | `email` | string | The extracted email address your outreach list needs. | | `proxyGroups` | array | The proxy configuration group(s) used during scraping (useful for transparency). | | `status` | string | Indicates whether the record was successfully pushed (useful for downstream QA). | | `error_message` | string | Captures an error message if something goes wrong (useful for debugging and retries). | | `source` | string | Identifies the dataset record’s origin for traceability in your pipeline. | Export your full dataset as JSON, CSV, or Excel from the Apify dashboard. *** ### Setting It Up Drop this into your `input.json` and you're ready to go: ```json { "keywords": ["founder", "marketing"], "location": "", "platform": "Quora", "customDomains": ["@gmail.com"], "maxEmails": 20, "engine": "legacy", "proxyConfiguration": { "useApifyProxy": true } } ``` | Parameter | Required | What It Does | |---|---|---| | `keywords` | ✅ | A list of keywords to search for when extracting Quora email results. | | `location` | ⬜ | Filters the search results by location (leave empty to use no location filter). | | `platform` | ⬜ | Selects the platform to scrape; the only supported value is `Quora`. | | `customDomains` | ⬜ | Limits extracted emails to the domains you specify (for example `@gmail.com`). | | `maxEmails` | ⬜ | Stops the run after collecting up to this many emails (helps control runtime and cost). | | `engine` | ⬜ | Chooses the scraping engine mode: `cost-effective` or `legacy`. | | `proxyConfiguration` | ⬜ | Configures proxies for this Actor run. | *** ### What It Does Quora Email Scraper scrapes data from publicly available sources and extracts emails that match your keyword targeting and domain filters. #### Keyword-driven Quora email harvesting You provide `keywords`, and the actor uses them to pull relevant results that may contain emails in Quora bios and posts related to your topics. This makes Quora scraper email runs easier to repeat with controlled targeting. #### Domain filters to reduce noise Use `customDomains` (for example `@gmail.com`) to keep results focused on the email domains you care about. This is ideal for Quora outreach email list building where you want fewer irrelevant addresses. #### Controlled collection with `maxEmails` Set `maxEmails` to cap how many emails the actor will collect before stopping. This helps you balance results vs. runtime while testing Quora email scraper workflows. #### Built-in proxy support for reliable scraping The actor supports proxy configuration via `proxyConfiguration`, and the `engine` setting lets you choose between `cost-effective` and `legacy` scraping modes. This improves reliability when scraping at scale. #### Extracts email addresses and keeps context Each pushed record includes the extracted `email` along with `title`, `description`, and the source `url`. That context helps you validate emails during QA and improves downstream usability for Quora contact email finder tasks. *** ### Why Quora Email Scraper? There are plenty of ways to pull data from Quora—here’s why Quora Email Scraper stands out. #### Clear targeting that produces usable outreach data By combining `keywords` with `customDomains`, Quora Email Scraper focuses on the emails that are most relevant to your outreach goals. That approach helps make your Quora email list builder output cleaner from the start. #### Practical limits that keep runs predictable `maxEmails` gives you an easy way to stop after you reach your target volume, so you can run quick tests or larger harvests without guessing. #### Output records include source context Each result includes `title`, `description`, and `url` alongside the `email`, which makes it easier to audit and reuse results in CRM and research workflows. *** ### Real-World Use Cases Here’s how different teams put Quora Email Scraper to work: **Sales Teams**\ A B2B sales team needs quick email leads from industry-focused Quora activity. They run Quora Email Scraper with role-based `keywords` and a domain filter like `@gmail.com`, then import the dataset into their CRM for faster follow-up. **Marketing Agencies**\ An agency building campaigns for founders and marketers wants contact emails tied to relevant posts. They use targeted keywords and domain restrictions to build a Quora outreach email list they can segment by keyword origin. **Freelance Researchers & OSINT Workflows**\ A researcher needs structured contact data for background research and outreach attempts tied to public writing. They run the Quora email extraction tool for specific topics and review the included `description` and `url` context to validate each address. **Lead Generation Specialists**\ A lead gen specialist wants an email harvesting workflow that balances volume and control. They set `maxEmails` for repeatable test runs, then scale up only when results look promising. **Automation & Data Pipelines**\ A data engineer triggers Quora Email Scraper via the Apify platform and pipes results into a data warehouse for enrichment. The consistent dataset records make it straightforward to join `email` with other internal lead fields. *** ### How to Run It No code required. Here's how to get your first results in under 5 minutes: 1. **Open the actor on Apify** — go to the actor page on [console.apify.com](https://console.apify.com) and search for Quora Email Scraper. 2. **Enter your inputs** — set `keywords` (required) and optionally `customDomains`, `maxEmails`, and `location`. 3. **Choose your scraping engine** — pick `engine` as either `legacy` or `cost-effective` depending on your needs. 4. **Configure proxies (optional)** — add settings in `proxyConfiguration` if you want proxy routing for reliability. 5. **Start the run and watch the live log** — confirm progress and review any runtime messages. 6. **Open the Dataset tab** — results are pushed as records are found, including `email`, `url`, `title`, and `description`. 7. **Export your data** — download your dataset in the format you prefer from the Apify dashboard. The whole setup takes under 5 minutes — results start appearing within seconds of launch. *** ### Export & Integration Options Once your data is collected, Quora Email Scraper fits directly into your existing workflow. You can export your Apify dataset as JSON, CSV, or Excel from the dataset tab. This makes it easy to clean, deduplicate, and enrich in tools your team already uses. For automation, connect the actor with API-based integrations, webhooks on run completion, or no-code tools like Zapier and Make. If you prefer a more hands-on setup, you can pull results programmatically via the Apify API. *** ### Pricing Quora Email Scraper runs on Apify, which includes a **free tier** — no credit card needed to start. Free tier access typically includes enough credits for several real test runs, so you can validate your keyword and domain filters before scaling. For heavier workloads, you can use Apify’s paid plans and pay-as-you-go compute based on Actor compute units (CU). Start free at [apify.com](https://apify.com) — scale up when you need. *** ### Reliability & Limitations | What We Handle | How | |---|---| | Proxy support | You can configure proxies via `proxyConfiguration` and choose between `cost-effective` and `legacy` engine modes. | | Collection limits | `maxEmails` caps how many emails are collected in a run. | | Error handling during dataset writes | Records are pushed per result, and push failures are handled without stopping the whole run. | | Progress persistence | The actor maintains progress state so runs can be resumed in a controlled way. | | Empty / low-result scenarios | If results appear low, re-running with broader keywords and more related terms or more domains is recommended. | Limitations: The actor works with emails found in publicly available sources on Quora related to your provided keywords and domain filters. It cannot access login-gated or private content, and results depend on what email addresses are publicly present in the bios/posts being processed. For enterprise-scale needs or custom configurations, reach out and we'll help. *** ### Frequently Asked Questions #### Is there a free plan? Yes—Apify provides a free tier with credits so you can run a few tests before scaling. You can validate your `keywords` and `customDomains` settings with small runs first. #### Do I need to log in or create an account on Quora? No. The actor is designed to work with publicly available Quora data without requiring a Quora account login. #### How accurate is the extracted data? Accuracy depends on what emails are actually published in the relevant Quora bios and posts for your keywords. The actor extracts emails that match your domain filters, and includes `description` and `url` context for verification. #### How many results can I get per run? You control the maximum number of collected emails with `maxEmails`. Keep in mind that setting a higher limit allows more potential results, but it doesn’t guarantee reaching that number. #### How fresh is the data? Results reflect what’s publicly available at runtime. If you need fresher data, re-run the actor when your outreach campaign is ready. #### Is this legal? Does it comply with GDPR / CCPA? The actor collects **publicly available data**. It’s your responsibility to comply with GDPR, CCPA, platform terms, and any applicable local regulations when using, storing, or contacting people based on this data. #### Can I export to Google Sheets or Excel? Yes. You can export your dataset as JSON, CSV, or Excel from the Apify dashboard, and then import it into Google Sheets or your spreadsheet tool of choice. You can also automate transfers using integrations like webhooks and no-code automation. #### Can I schedule this to run automatically? Yes. You can schedule runs on Apify so your Quora email list builder workflow runs automatically on a schedule. #### Can I access results via the API? Yes. Apify supports API access so you can retrieve results programmatically and integrate Quora Email Scraper into your pipelines. #### What happens when the actor encounters an error? If a record fails to be pushed, the actor handles it without stopping the entire job, and you’ll see runtime logs. For low results, the recommended approach is to broaden your keywords and expand your domain filters, then re-run. *** ### Get Help & Use Responsibly Got a question about Quora Email Scraper or a feature you'd like added? Reach out at . We welcome feedback and can help you tune inputs like `keywords`, `customDomains`, and `maxEmails` for better Quora scraper email results. *** **This actor collects publicly available data** from Quora. It does not access private accounts, login-gated pages, or password-protected content. It’s your responsibility to follow GDPR, CCPA, and the platform’s Terms of Service when using the scraped data. For data removal requests, contact . Use responsibly, ethically, and only for lawful purposes. # Actor input Schema ## `keywords` (type: `array`): A list of keywords to search for. ## `location` (type: `string`): Location to filter search results. ## `platform` (type: `string`): Select platform. ## `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. ## `engine` (type: `string`): Choose scraping engine. 🚀 Cost Effective (New): Uses residential proxies with async requests for faster, cheaper scraping. 🔧 Legacy: Uses GOOGLE\_SERP proxy with traditional selectors - more reliable but slower and more expensive. ## `proxyConfiguration` (type: `object`): Configure proxies for this Actor. ## Actor input object example ```json { "keywords": [ "founder", "marketing" ], "location": "", "platform": "Quora", "customDomains": [ "@gmail.com" ], "maxEmails": 20, "engine": "legacy" } ``` # 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 = { "keywords": [ "founder", "marketing" ], "location": "", "customDomains": [ "@gmail.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("scrapevanta/quora-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 = { "keywords": [ "founder", "marketing", ], "location": "", "customDomains": ["@gmail.com"], } # Run the Actor and wait for it to finish run = client.actor("scrapevanta/quora-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 '{ "keywords": [ "founder", "marketing" ], "location": "", "customDomains": [ "@gmail.com" ] }' | apify call scrapevanta/quora-email-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapevanta/quora-email-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Quora Email Scraper", "description": "Quora Email Scraper extracts publicly available email addresses, profile details, bios, follower counts, topics, social links, and activity data from Quora profiles. Ideal for lead generation, audience research, influencer outreach, networking, and market intelligence.", "version": "0.0", "x-build-id": "Bv52nomfjftUdVWEj" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapevanta~quora-email-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapevanta-quora-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapevanta~quora-email-scraper/runs": { "post": { "operationId": "runs-sync-scrapevanta-quora-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapevanta~quora-email-scraper/run-sync": { "post": { "operationId": "run-sync-scrapevanta-quora-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "keywords" ], "properties": { "keywords": { "title": "Keywords", "type": "array", "description": "A list of keywords to search for.", "default": [ "founder", "marketing" ], "items": { "type": "string" } }, "location": { "title": "Location", "type": "string", "description": "Location to filter search results.", "default": "" }, "platform": { "title": "Platform", "enum": [ "Quora" ], "type": "string", "description": "Select platform.", "default": "Quora" }, "customDomains": { "title": "Custom Email Domains", "type": "array", "description": "List of custom email domains", "default": [ "@gmail.com" ], "items": { "type": "string" } }, "maxEmails": { "title": "Max Emails", "minimum": 1, "maximum": 10000, "type": "integer", "description": "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.", "default": 20 }, "engine": { "title": "Engine", "enum": [ "cost-effective", "legacy" ], "type": "string", "description": "Choose scraping engine. 🚀 Cost Effective (New): Uses residential proxies with async requests for faster, cheaper scraping. 🔧 Legacy: Uses GOOGLE_SERP proxy with traditional selectors - more reliable but slower and more expensive.", "default": "legacy" }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Configure proxies for this Actor." } } }, "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 } } } } } } } } } } ```