# Glassdoor Community Qa Spider (`getdataforme/glassdoor-community-qa-spider`) Actor Extract insights from Glassdoor's community Q\&A sections with this Apify Actor. Scrape posts, comments, reactions, and metadata for job market trends and sentiments. Features robust error handling, fast performance, flexible queries, and structured JSON output.... - **URL**: https://apify.com/getdataforme/glassdoor-community-qa-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 --- ## Glassdoor Community Qa Spider The Glassdoor Community Qa Spider is a powerful Apify Actor designed to scrape and extract data from Glassdoor's community Q&A sections. It enables users to gather insights from user-generated posts, comments, reactions, and metadata related to job searches, career advice, and industry discussions. This tool is invaluable for researchers, recruiters, and businesses seeking to analyze trends in the job market and community sentiments. ### Features - **Comprehensive Data Extraction**: Scrapes posts, comments, reactions, likes, shares, and detailed metadata from Glassdoor community Q&A threads. - **High Reliability**: Built with robust error handling to ensure consistent data retrieval even from dynamic web pages. - **Fast Performance**: Optimized for speed, processing multiple queries efficiently without compromising data quality. - **Flexible Query Support**: Accepts multiple search queries to target specific topics or keywords in community discussions. - **Structured Output**: Delivers clean, JSON-formatted data ready for analysis, export, or integration with other tools. - **No-Code Operation**: Easy to configure and run via the Apify platform, requiring no programming skills. - **Scalable**: Handles large volumes of data, making it suitable for extensive research projects. ### Input Parameters | Parameter | Type | Required | Description | Example | |-----------|---------|----------|-------------|---------| | Queries | array | No | An array of search queries to target specific topics in Glassdoor community Q&A. Each query should be a string representing keywords or phrases. | ["community qa", "job search tips"] | ### Example Usage #### Input Configuration ```json { "Queries": ["community qa"] } ```` #### Output Example ```json [ { "search_query": "community qa", "id": "690ec16acdadaacdff9b2bf3", "url": "", "handleUrl": "hi-all-im-currently-looking-for-entry-level-opportunities-in-quality-assurance-qa-including-manual-automation-or-software-o694-5", "feedId": "633f4081cb7db7004cb99388", "feedName": "Jobs in STEM", "creationDate": "1762574698745", "messageType": "TEXT", "commentsCount": 5, "sharesCount": 0, "messageData": { "__typename": "CommunitySearchMessageData", "imageHeight": null, "imageUrl": null, "imageWidth": null, "linkMetadata": null, "text": "Hi all, I\u2019m currently looking for entry-level opportunities in Quality Assurance (QA), including manual, automation, or software testing roles. I have hands-on experience with test case design, defect tracking, and tools like Jira, Postman, and Selenium, and I\u2019m passionate about delivering high-quality software.\n\nAny recommendations or leads for entry-level QA positions or companies currently hiring would be greatly appreciated!", "video": null }, "companyMentions": [], "likes": [], "reactionCounters": { "__typename": "CommunitySearchReactionCounter", "funny": 0, "helpful": 0, "like": 1, "smart": 0, "uplifting": 0 }, "sign": { "__typename": "CommunitySearchSign", "displayNumber": null, "prefixEnum": null, "profileImage": null, "type": "TITLE", "value": "QA Engineer" }, "comments": [], "actor_id": "bd5n8MEaG4j683uLd", "run_id": "iag4rpZJ9k8GD5yq5" } ] ``` ### Use Cases - **Market Research**: Analyze community discussions to identify emerging trends in job markets and industry demands. - **Competitive Intelligence**: Gather insights on competitor hiring practices and employee sentiments from Glassdoor posts. - **Job Market Analysis**: Track entry-level opportunities and career advice in specific fields like QA or engineering. - **Content Aggregation**: Collect user-generated content for blogs, reports, or social media analysis. - **Academic Research**: Study community interactions and reactions for sociological or economic studies. - **Business Automation**: Automate data collection for HR departments monitoring recruitment trends. ### Installation and Usage 1. Search for "Glassdoor Community Qa 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 an array of JSON objects, each representing a community post. Key fields include: - `search_query`: The query used to find the post. - `id`: Unique identifier for the post. - `handleUrl`: URL-friendly handle for the post. - `feedName`: Name of the community feed (e.g., "Jobs in STEM"). - `creationDate`: Timestamp of post creation. - `messageData.text`: The full text content of the post. - `reactionCounters`: Counts of reactions like likes, helpful, etc. - `commentsCount`: Number of comments on the post. - `sign.value`: Author's title or role (e.g., "QA Engineer"). - `actor_id` and `run_id`: Metadata for the scraping run. Data is structured for easy parsing and integration. ### 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": [ "community qa" ] } ``` # 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/glassdoor-community-qa-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/glassdoor-community-qa-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/glassdoor-community-qa-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/glassdoor-community-qa-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Glassdoor Community Qa Spider", "description": "Extract insights from Glassdoor's community Q&A sections with this Apify Actor. Scrape posts, comments, reactions, and metadata for job market trends and sentiments. Features robust error handling, fast performance, flexible queries, and structured JSON output....", "version": "0.0", "x-build-id": "ZzSuyifvdhY5WAVst" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~glassdoor-community-qa-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-glassdoor-community-qa-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~glassdoor-community-qa-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-glassdoor-community-qa-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~glassdoor-community-qa-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-glassdoor-community-qa-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "community qa" ], "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 } } } } } } } } } } ```