# Quora Questions Spider (`getdataforme/quora-questions-spider`) Actor The Quora Questions Spider is a tool for scraping questions from Quora based on user-defined queries. It extracts comprehensive data, including question texts and metadata, with customizable query options and item limits.... - **URL**: https://apify.com/getdataforme/quora-questions-spider.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **Stats:** 5 total users, 1 monthly users, 93.1% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 Questions Spider ### Introduction The Quora Questions Spider is a powerful tool designed to scrape questions from Quora based on specified queries. It efficiently gathers data such as question text, URLs, and metadata, providing valuable insights for various applications. ### Features - **Comprehensive Data Extraction**: Captures detailed information including question IDs, texts, URLs, number of answers, followers, and more. - **Customizable Queries**: Allows users to specify multiple search queries tailored to their needs. - **Flexible Item Limiting**: Users can set a maximum number of items to scrape per run or opt for unlimited extraction. - **High Data Quality**: Ensures reliable and accurate data collection from Quora. - **User-Friendly Interface**: Easy configuration with intuitive input parameters. ### Input Parameters Table | Parameter | Type | Required | Description | Example | |------------|---------|----------|-----------------------------------------------------------------------------|--------------------------| | Queries | array | No | The queries for the spider. | `["Cooking", "Travel"]` | | item_limit | integer | No | Maximum items to scrape per actor run. Set to 0 for no limit. | `10` | ### Example Usage #### Input JSON ```json { "Queries": ["Cooking"], "item_limit": 5 } ```` #### Output JSON ```json [ { "question_id": "UXVlc3Rpb25AMDoxMTg5Nzc5Ng==", "question_text": "What is the monthly cost of postpaid number in Nepal Telecom?", "question_url": "https://www.quora.com/What-is-the-monthly-cost-of-postpaid-number-in-Nepal-Telecom", "num_answers": 3, "created_date": null, "scraped_at": "2026-05-22T06:42:34.058553+00:00", "topic_tags": [], "num_followers": 1, "last_answered_date": null, "actor_id": null, "run_id": null }, { "question_id": "UXVlc3Rpb25AMDozNTgxMzE1", "question_text": "How do I deactivate Nepal Telecom news pack?", "question_url": "https://www.quora.com/unanswered/How-do-I-deactivate-Nepal-Telecom-news-pack", "num_answers": 0, "created_date": null, "scraped_at": "2026-05-22T06:42:34.058553+00:00", "topic_tags": [], "num_followers": 1, "last_answered_date": null, "actor_id": null, "run_id": null }, { "question_id": "UXVlc3Rpb25AMDoxNDc4NjY5OA==", "question_text": "What is the monthly salary of computer engineer working in Nepal Telecom?", "question_url": "https://www.quora.com/What-is-the-monthly-salary-of-computer-engineer-working-in-Nepal-Telecom", "num_answers": 2, "created_date": null, "scraped_at": "2026-05-22T06:42:34.058553+00:00", "topic_tags": [], "num_followers": 4, "last_answered_date": null, "actor_id": null, "run_id": null } ] ``` ### Use Cases - **Market Research and Analysis**: Gather insights on trending topics and consumer interests. - **Competitive Intelligence**: Monitor competitors' questions to understand their focus areas. - **Price Monitoring**: Track discussions around pricing strategies in various industries. - **Content Aggregation**: Compile relevant questions for content creation or research purposes. - **Academic Research**: Use data for studies related to social media trends and user engagement. - **Business Automation**: Automate the collection of industry-specific queries for strategic planning. ### Installation and Usage 1. Search for "Quora Questions Spider" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters as needed. 4. Click "Start" to begin extraction. 5. Monitor progress in the log. 6. Export results in your preferred format (JSON, CSV, Excel). ### Output Format The output is a JSON array of objects, each representing a question with fields such as `question_id`, `question_text`, `question_url`, `num_answers`, `scraped_at`, `topic_tags`, `num_followers`, and more. This structured format allows for easy integration into data analysis workflows. ### Error Handling The spider includes error handling to manage common issues like network failures or changes in Quora's HTML structure. If an error occurs, it logs detailed information to help diagnose the problem. Users are encouraged to check the log for any errors and adjust their configurations accordingly. ### Rate Limiting and Best Practices To avoid being blocked by Quora, adhere to rate limiting guidelines: - Implement delays between requests. - Randomize user-agent strings if possible. - Respect Quora's terms of service to ensure ethical scraping practices. ### Limitations and Considerations - **Rate Limits**: Be mindful of Quora's rate limits to prevent IP bans. - **Data Completeness**: Some data fields may be null or unavailable due to changes in Quora's page structure. - **Legal Compliance**: Ensure compliance with legal requirements regarding web scraping and data usage. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: [Contact Us](https://getdataforme.com/contact/) We're here to help you get the most out of this Actor! # Actor input Schema ## `Queries` (type: `array`): The queries for the spider. ## `item_limit` (type: `integer`): Maximum items to scrape per actor run. Set to 0 for no limit. ## Actor input object example ```json { "Queries": [ "Cooking" ], "item_limit": 10 } ``` # Actor output Schema ## `results` (type: `string`): Scraped data items from dataset # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/quora-questions-spider").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 = {} # Run the Actor and wait for it to finish run = client.actor("getdataforme/quora-questions-spider").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 '{}' | apify call getdataforme/quora-questions-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/quora-questions-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Quora Questions Spider", "description": "The Quora Questions Spider is a tool for scraping questions from Quora based on user-defined queries. It extracts comprehensive data, including question texts and metadata, with customizable query options and item limits....", "version": "0.0", "x-build-id": "DqhNT6SKXzS8DgAvr" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~quora-questions-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-quora-questions-spider", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~quora-questions-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-quora-questions-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~quora-questions-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-quora-questions-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "Queries": { "title": "Queries", "type": "array", "description": "The queries for the spider.", "default": [ "Cooking" ], "items": { "type": "string" } }, "item_limit": { "title": "Item limit", "minimum": 0, "type": "integer", "description": "Maximum items to scrape per actor run. Set to 0 for no limit.", "default": 10 } } }, "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 } } } } } } } } } } ```