# Profanity Detection (`greip/profanity-detection`) Actor Analyze text content for profanity, offensive language, and inappropriate content with Machine Learning. Detects harmful language, provides risk scoring, and helps maintain safe user environments by filtering inappropriate content. - **URL**: https://apify.com/greip/profanity-detection.md - **Developed by:** [Greip](https://apify.com/greip) (community) - **Categories:** AI, Developer tools, Other - **Stats:** 5 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.10 / 1,000 requests This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## Greip's Profanity Detection A robust Apify Actor that detects offensive and inappropriate language in text using the **Greip Profanity Detection API**. This tool helps safeguard your website or app by screening user inputs for profanity and other harmful content, maintaining a positive user environment and protecting your brand. ### 🚀 Features - **ML-Powered Detection**: Advanced Machine Learning algorithms to analyze text content - **Risk Scoring**: Numerical risk scores from 0-3 (0=safe, 1=high-risk, 2=medium-risk, 3=low-risk) - **Bad Words Identification**: Detailed list of detected profane words when ML detection is used - **Real-time Analysis**: Fast processing with execution time tracking - **Structured Output**: Clean, well-formatted JSON results stored in datasets ### Input Parameters | Parameter | Type | Required | Description | | --------- | ------ | -------- | ------------------------------------- | | `text` | string | ✅ | Text content to analyze for profanity | | `userID` | string | ❌ | Optional user identifier for tracking | ### Output Format The Actor outputs detailed analysis information for each text input: ```json { "text": "This is a sample text to analyze", "isSafe": true, "riskScore": 0, "totalBadWords": 0, "isML": true, "badWords": [], "timestamp": "2026-01-14T10:30:00.000Z" } ```` #### Output Field Descriptions **Profanity Analysis Fields:** - `text`: The original text that was analyzed - `isSafe`: Boolean indicating whether the text is safe (true) or contains profanity (false) - `riskScore`: Risk assessment score: - `0` = Completely safe text - `1` = High-risk text (most dangerous profanity) - `2` = Medium-risk text (moderate profanity) - `3` = Low-risk text (mild profanity) - `totalBadWords`: Total number of profane words detected (only available when `isML` is true) - `isML`: Boolean indicating whether Machine Learning detection was used - `badWords`: Array of detected profane words (only populated when ML detection finds issues) - `timestamp`: ISO 8601 timestamp when the analysis was processed ### Use Cases - **Content Moderation**: Screen user comments, posts, and messages before publication - **User Registration**: Detect offensive usernames during account creation - **Chat Systems**: Real-time filtering of chat messages and communication - **Review Systems**: Moderate product reviews and feedback - **Social Media**: Monitor user-generated content for community guidelines compliance ### Your feedback We're always working on improving the performance of our Actors. If you have any technical feedback for the profanity detection actor or found a bug, please create an issue in the [Issues tab](https://apify.com/greip/profanity-detection/issues/open). # Actor input Schema ## `text` (type: `string`): The text content to analyze for profanity, offensive language, or inappropriate content. Can include user comments, posts, messages, usernames, or any other text-based input. ## `listBadWords` (type: `boolean`): Set to true to return an array of detected profane or offensive words found in the text. This helps identify specific problematic terms. ## `scoreOnly` (type: `boolean`): Set to true to return only the risk score and safety status without additional details. Useful for quick filtering and performance optimization. ## `userID` (type: `string`): Optional user identifier for tracking requests in the Greip dashboard (e.g., email address, user ID, username). Helps monitor and analyze user-specific patterns. ## Actor input object example ```json { "text": "This is a sample text to check for profanity and inappropriate content.", "listBadWords": false, "scoreOnly": false } ``` # Actor output Schema ## `profanityAnalysis` (type: `string`): No description # 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 = { "text": "This is a sample text to check for profanity and inappropriate content." }; // Run the Actor and wait for it to finish const run = await client.actor("greip/profanity-detection").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 = { "text": "This is a sample text to check for profanity and inappropriate content." } # Run the Actor and wait for it to finish run = client.actor("greip/profanity-detection").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 '{ "text": "This is a sample text to check for profanity and inappropriate content." }' | apify call greip/profanity-detection --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=greip/profanity-detection", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Profanity Detection", "description": "Analyze text content for profanity, offensive language, and inappropriate content with Machine Learning. Detects harmful language, provides risk scoring, and helps maintain safe user environments by filtering inappropriate content.", "version": "0.2", "x-build-id": "LX7uZpjHQsYIql3b0" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/greip~profanity-detection/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-greip-profanity-detection", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/greip~profanity-detection/runs": { "post": { "operationId": "runs-sync-greip-profanity-detection", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/greip~profanity-detection/run-sync": { "post": { "operationId": "run-sync-greip-profanity-detection", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "text" ], "properties": { "text": { "title": "Text to Analyze", "type": "string", "description": "The text content to analyze for profanity, offensive language, or inappropriate content. Can include user comments, posts, messages, usernames, or any other text-based input." }, "listBadWords": { "title": "List Bad Words", "type": "boolean", "description": "Set to true to return an array of detected profane or offensive words found in the text. This helps identify specific problematic terms.", "default": false }, "scoreOnly": { "title": "Score Only", "type": "boolean", "description": "Set to true to return only the risk score and safety status without additional details. Useful for quick filtering and performance optimization.", "default": false }, "userID": { "title": "User Identifier", "type": "string", "description": "Optional user identifier for tracking requests in the Greip dashboard (e.g., email address, user ID, username). Helps monitor and analyze user-specific patterns." } } }, "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 } } } } } } } } } } ```