# Quora Answers Discovery Spider (`getdataforme/quora-answers-discovery-spider`) Actor Discover and extract Quora answers with this Apify Actor. Input queries to gather rich insights like text, authors, upvotes, and timestamps for trend analysis. Perfect for researchers and marketers. Features reliable scraping, scalable performance, and clean JSON output. - **URL**: https://apify.com/getdataforme/quora-answers-discovery-spider.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN 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 Answers Discovery Spider The Quora Answers Discovery Spider is an Apify Actor designed to scrape and extract answers from Quora based on user-defined queries. It provides a reliable way to gather detailed insights, including answer text, author information, upvotes, and timestamps, enabling users to analyze trends and opinions on various topics. This tool is perfect for researchers, marketers, and businesses seeking to harness the wealth of user-generated content on Quora. ### Features - **Targeted Querying**: Supports multiple queries to fetch relevant answers from Quora's vast database. - **Comprehensive Data Extraction**: Captures rich metadata such as author details, upvotes, shares, and timestamps for in-depth analysis. - **High Reliability**: Built with robust error handling to ensure consistent data retrieval even with varying query complexities. - **Scalable Performance**: Efficiently processes large volumes of data, making it suitable for extensive research projects. - **JSON Output**: Delivers clean, structured JSON data ready for integration into databases or analytics tools. - **User-Friendly Interface**: Easy configuration through Apify's platform, with no coding required. - **Ethical Scraping**: Adheres to Quora's terms of service and respects rate limits to avoid disruptions. ### Input Parameters | Parameter | Type | Required | Description | Example | |-----------|--------|----------|-------------|---------| | Queries | array | No | A list of search queries to scrape answers for. Each query should be a string representing a topic or keyword on Quora. | ["Cooking", "Machine Learning"] | ### Example Usage To run the Actor, provide input in JSON format. Here's an example: ```json { "Queries": ["Cooking", "Vegetarian Recipes"] } ```` The Actor will output a JSON array of answer objects. Example output: ```json [ { "answer_id": "146404715", "parent_question_id": "17351318", "answer_text": "When you are in foreign countries you should know how to cook especially if you are pure vegetarian. Also, situations teach you so many things like cooking.\nGermany is a big meat eating country. It is very hard to live having only salad as an option. Because according to them, vegetarian means salad.\nSo, I start learning cooking. I use the YouTube channel of #kabita\u2019s kitchen where I learned a lot of vegetarian recipes.\nI cooked some vegetarian dishes on my own. I\u2019m sharing some pics of my cooked food.\nRice, Daal, Mix Veg\n2. Rajma Chawal\n3. Couscous (Kind of Upma)\n4. Khaman .\n5. Rice, Butter paneer masala\n6. Dosa (My favourite)\n7. Fruit salad\n8. Prathe, Toor Daal\n9. Pav-Bhaji\n10. Cheese pasta\n11. Puri, Matar paneer\n12. Poha\n13. Palak daal\n14. Idli & Shambhar\n15. Daal Bati\nSo, these are some dishes.\nNo comparison of Indian cuisine!!!", "author_name": "Divya Singhal (\u0926\u093f\u0935\u094d\u092f)", "author_credentials": "Learning and cooking for survival in Germany", "author_followers": 622, "upvotes": 3008, "num_shares": 27, "answer_date": "2019-06-09T06:22:07.232873+00:00", "is_collapsed": false, "scraped_at": "2026-05-07T09:31:53.534956+00:00", "actor_id": "knhVi4FFQGM4Vf7MS", "run_id": "9wWatlzbhvKsQes3v" }, { "answer_id": "126837249", "parent_question_id": "1201508", "answer_text": "1.. While making Upma, lumps get formed while adding rava to the hot boiling tempered water.\nTo avoid this, add rava first and then pour hot water in it and then stir just twice.\nNo lumps anymore.\n2.. To make a good masala gravy, grind tomatoes in whipper mode and then saute it.\nThis increases the quantity of gravy too.\n3.. While making Idly podi, add skinned urad dal to make it more healthy.\nAfter roasting it, dry it in the Sun to increase the shelf life period (till 1 year).\n4.. Sometimes, oil goes in excess while making fries like Ladies finger, bitter gourd etc.. So the fries get soggy in oil.\nTo remove the excess oil, add a combination of rice flour, besan flour and corn flour or one among the three and this flour will absorb the excess oil and makes the fry more crispy.\nOne can also add grated coconut to absorb oil and also to improve the flavour in fries like ladies finger.\n5.. Never add room temperature water to a cooked meat, it will make the meat hard. So always add hot water to your cooked meat or cooking meat.\n6.. Wash meat with turmeric.\n7.. Instead of adding matured curry leaves, add tender curry leaves as people can easily chew and eat it.\n8.. While making curd rice, pour some amount of strained boiled rice water as it improves the flavour. And this can also be used when you don't have sufficient curd to make curd rice.\n9.. Sometimes, we forget to soak cereals and pulses in water overnight to cook it next day.\nWhat you can do is soak it in hot water for half an hour before boiling it.\nOthers tend to soak it with normal water with half spoon of cooking soda, half an hour before pressure cooking it.\nAdding cooking soda also helps in retaining the colour of the green vegetables while boiling.\n10.. While making Parotha, add milk to it as it softens the dough (wheat flour and refined wheat flour).\n11.. Do not throw the seeds from vegetables like Pumpkins.\nDry it in the sun, break the outer layer and add the inside seeds to drinks like Badam Milk as they are really healthy.\nMake sure that the seeds are from matured pumpkins.", "author_name": "Arokiya Monica", "author_credentials": "", "author_followers": 7807, "upvotes": 3553, "num_shares": 52, "answer_date": "2019-03-09T07:01:03.106681+00:00", "is_collapsed": false, "scraped_at": "2026-05-07T09:31:53.534956+00:00", "actor_id": "knhVi4FFQGM4Vf7MS", "run_id": "9wWatlzbhvKsQes3v" }, { "answer_id": "1477743727373912", "parent_question_id": "139599897", "answer_text": "", "author_name": "George Cook", "author_credentials": "former Software Developer (2019-2023)", "author_followers": 1299, "upvotes": 15365, "num_shares": 633, "answer_date": "2023-12-30T14:23:26.353391+00:00", "is_collapsed": false, "scraped_at": "2026-05-07T09:31:53.534956+00:00", "actor_id": "knhVi4FFQGM4Vf7MS", "run_id": "9wWatlzbhvKsQes3v" } ] ``` ### Use Cases - **Market Research**: Analyze consumer opinions on products or services by querying related topics. - **Competitive Intelligence**: Monitor discussions about competitors to identify trends and feedback. - **Content Aggregation**: Collect expert answers for blog posts, articles, or educational materials. - **Academic Research**: Gather qualitative data on social issues, technologies, or cultural topics. - **Business Automation**: Automate data collection for sentiment analysis or trend reporting. - **SEO and Marketing**: Discover popular questions and answers to inform content strategies. ### Installation and Usage 1. Search for "Quora Answers Discovery Spider" in the Apify Store 2. Click "Try for free" or "Run" 3. Configure input parameters 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 Quora answer. Key fields include: - `answer_id`: Unique identifier for the answer. - `parent_question_id`: ID of the related question. - `answer_text`: The full text of the answer. - `author_name`: Name of the answer's author. - `author_credentials`: Author's credentials or bio. - `author_followers`: Number of followers. - `upvotes`: Number of upvotes. - `num_shares`: Number of shares. - `answer_date`: Timestamp of when the answer was posted. - `is_collapsed`: Boolean indicating if the answer is collapsed. - `scraped_at`: Timestamp of when the data was scraped. - `actor_id` and `run_id`: Metadata from the Apify run. Note: Some answers may have empty `answer_text` if collapsed or unavailable. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: 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. ## Actor input object example ```json { "Queries": [ "Cooking" ] } ``` # 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-answers-discovery-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-answers-discovery-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-answers-discovery-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/quora-answers-discovery-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Quora Answers Discovery Spider", "description": "Discover and extract Quora answers with this Apify Actor. Input queries to gather rich insights like text, authors, upvotes, and timestamps for trend analysis. Perfect for researchers and marketers. Features reliable scraping, scalable performance, and clean JSON output.", "version": "0.0", "x-build-id": "z4ZrgeikVxmTT13qe" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~quora-answers-discovery-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-quora-answers-discovery-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~quora-answers-discovery-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-quora-answers-discovery-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~quora-answers-discovery-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-quora-answers-discovery-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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" } } } }, "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 } } } } } } } } } } ```