# AI Content Detector (`junipr/ai-content-detector`) Actor Detect AI-generated text using statistical analysis. Analyzes perplexity, burstiness, vocabulary diversity, repetition patterns, and writing style. Returns confidence scores, classification (AI/human/mixed), and detailed signal breakdowns. Batch processing for multiple texts. - **URL**: https://apify.com/junipr/ai-content-detector.md - **Developed by:** [junipr](https://apify.com/junipr) (community) - **Categories:** Developer tools, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $6.50 / 1,000 text analyzeds 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 ## AI Content Detector Detect AI-generated text from modern LLMs using statistical analysis. Analyzes perplexity patterns, sentence burstiness, vocabulary diversity, repetition patterns, and stylistic uniformity — all running **locally with zero external API calls**. ### Why Use This Actor Existing AI detection tools require expensive subscriptions ($15+/month for GPTZero, Originality.ai) and send your text to external AI services. This actor uses proven statistical indicators running entirely on Apify's infrastructure. | Feature | Ours | GPTZero | Originality.ai | Copyleaks | |---------|------|---------|----------------|-----------| | Price per 1K texts | **$6.50** | ~$10-15/sub | ~$15/sub | ~$10/sub | | Batch processing | ✅ 100K+ | Limited | Limited | Yes | | External API needed | **None** | Proprietary | Proprietary | Proprietary | | Speed | **<10ms/text** | ~500ms | ~500ms | ~500ms | | Deterministic results | ✅ | ❌ | ❌ | ❌ | | Open methodology | ✅ | ❌ | ❌ | ❌ | **Honest limitation:** Statistical AI detection is not perfect. No detector achieves 100% accuracy. Short texts (<100 words), heavily edited AI text, and formal/technical writing may produce less accurate results. Use probability scores as signals, not verdicts. ### How to Use Zero-config: just provide an array of text strings. ```json { "texts": [ "In today's rapidly evolving technological landscape, it is important to note that artificial intelligence has demonstrated remarkable capabilities across a wide variety of domains.", "I can't believe how weird yesterday was. My dog somehow got into the kitchen and ate half a pizza while I was on a call. The whole thing was chaos." ] } ```` With metadata and IDs: ```json { "texts": [ { "id": "submission-456", "text": "The article text to analyze...", "metadata": { "author": "John Doe", "source": "blog" } } ] } ``` ### Input Configuration | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `texts` | array | Demo texts | Plain strings or objects with text/id/metadata. Max: 100,000 | | `analyzePerplexity` | boolean | `true` | Word predictability analysis | | `analyzeBurstiness` | boolean | `true` | Sentence length variation | | `analyzeVocabulary` | boolean | `true` | Type-token ratio, vocabulary diversity | | `analyzeRepetition` | boolean | `true` | Filler phrases, transitional word density | | `analyzeStyle` | boolean | `true` | Paragraph uniformity, sentence starters | | `paragraphLevel` | boolean | `true` | Per-paragraph analysis for mixed content | | `aiThreshold` | number | `0.65` | AI classification threshold (0.50–0.95) | | `includeSignalDetails` | boolean | `true` | Full signal breakdown in output | | `onlyAiDetected` | boolean | `false` | Filter to AI-detected texts only | ### Output Format ```json { "id": null, "classification": "ai_generated", "aiProbability": 0.78, "humanProbability": 0.22, "confidence": 0.82, "wordCount": 245, "signals": { "burstiness": { "score": 0.71, "sentenceLengthVariance": 8.2, "sentenceLengthMean": 22.4, "interpretation": "Sentence lengths are suspiciously uniform — AI-like pattern", "weight": 0.25 }, "repetition": { "score": 0.69, "fillerPhraseCount": 4, "transitionalWordDensity": 0.062, "interpretation": "High density of AI-characteristic phrases" } }, "flaggedPatterns": [ { "pattern": "uniform_sentence_length", "description": "Sentences are unusually similar in length", "severity": "strong" }, { "pattern": "filler_phrase_density", "description": "4 AI-typical filler phrases detected", "severity": "moderate" } ], "paragraphAnalysis": [ { "paragraphIndex": 1, "aiProbability": 0.82, "classification": "ai_generated", "dominantSignal": "repetition" } ], "analyzedAt": "2026-03-11T12:00:00.000Z" } ``` #### Signal Score Interpretation | Range | Meaning | |-------|---------| | 0.0 – 0.3 | Strongly suggests human writing | | 0.3 – 0.5 | Mildly suggests human writing | | 0.5 – 0.7 | Uncertain | | 0.7 – 1.0 | Strongly suggests AI writing | ### Detection Signals - **Perplexity:** AI text uses consistently "safe" word choices — high predictability, low variance - **Burstiness:** Human text has highly variable sentence lengths; AI text is uniform - **Vocabulary:** AI text often repeats vocabulary at higher rates (lower type-token ratio) - **Repetition:** AI text overuses transitional words ("furthermore", "moreover") and filler phrases - **Style:** AI text tends to have uniform paragraph lengths and repetitive sentence starters ### Tips and Advanced Usage - **Paragraph-level analysis:** Enable `paragraphLevel: true` to detect mixed content (human intro + AI body) - **Adjusting threshold:** Lower threshold (0.50) = more aggressive, higher false positive rate; Higher (0.85) = only flag clear AI text - **False positives:** Formal academic and technical writing may score as AI-like — check `flaggedPatterns` for context - **Batch audits:** Use `onlyAiDetected: true` to filter a large content set to flagged items for manual review ### Pricing **$6.50 per 1,000 texts analyzed** (PPE — pay only for successfully analyzed texts) Pricing includes all platform compute costs — no hidden fees. | Use Case | Texts | Cost | |----------|-------|------| | Student assignment check | 30 | $0.20 | | Blog content audit | 500 | $3.25 | | Monthly submission screening | 5,000 | $32.50 | | Platform content moderation | 100,000 | $650.00 | Texts below 50 characters and empty inputs are NOT billed. ### FAQ #### How accurate is this detector? Statistical detection typically achieves 70-85% accuracy on unmodified AI text. Heavily edited AI text and formal human writing reduce accuracy. Always treat results as probability signals, not proof. #### Can it identify which AI model wrote the text? No — it detects AI-like statistical patterns, not model fingerprints. Outputs from modern LLMs tend to score similarly. #### What's the minimum text length? 50 characters minimum. For reliable results, 200+ words recommended. #### Does it work on non-English text? Most signals work across languages (burstiness, vocabulary diversity). Filler phrase detection is English-only. #### Is this suitable for academic integrity checking? As one signal among many, yes. Do not use as sole evidence of misconduct — human review is always recommended. #### How does it differ from GPTZero? GPTZero uses a proprietary AI model (which itself has biases). This actor uses transparent, deterministic statistical methods — same input always produces same output, and you can understand exactly why a text was flagged. *** Related actors by Junipr: [Address Validator](https://apify.com/junipr/address-validator) | [Temporary Email Generator](https://apify.com/junipr/temporary-email-generator) # Actor input Schema ## `texts` (type: `array`): List of texts to analyze for AI content. Each can be a plain string or an object with text, id, and metadata fields. ## `analyzePerplexity` (type: `boolean`): Analyze word-level perplexity (predictability). AI text tends to have uniformly low perplexity. ## `analyzeBurstiness` (type: `boolean`): Analyze sentence length variation. Human text has high burstiness; AI text is more uniform. ## `analyzeVocabulary` (type: `boolean`): Analyze vocabulary diversity (type-token ratio). AI text often has lower vocabulary diversity. ## `analyzeRepetition` (type: `boolean`): Detect phrase repetition patterns and filler phrases common in AI text. ## `analyzeStyle` (type: `boolean`): Analyze stylistic uniformity (paragraph length variation, punctuation patterns). ## `paragraphLevel` (type: `boolean`): Analyze each paragraph separately to detect mixed human+AI content. ## `aiThreshold` (type: `number`): Probability threshold above which text is classified as likely AI-generated. Min: 0.50, Max: 0.95. ## `includeSignalDetails` (type: `boolean`): Include detailed breakdown of each analysis signal. ## `onlyAiDetected` (type: `boolean`): Only output texts classified as AI-generated. ## `onlyHumanDetected` (type: `boolean`): Only output texts classified as human-written. ## Actor input object example ```json { "texts": [ "The quick brown fox jumps over the lazy dog. This is a simple sentence written naturally.", "In the realm of artificial intelligence, large language models have demonstrated remarkable capabilities in generating human-like text across a wide variety of domains and applications. Furthermore, it is important to note that these systems exhibit consistent patterns." ], "analyzePerplexity": true, "analyzeBurstiness": true, "analyzeVocabulary": true, "analyzeRepetition": true, "analyzeStyle": true, "paragraphLevel": true, "aiThreshold": 0.65, "includeSignalDetails": true, "onlyAiDetected": false, "onlyHumanDetected": false } ``` # Actor output Schema ## `results` (type: `string`): AI probability scores, human/AI classification, confidence, perplexity/burstiness/vocabulary/repetition/style signal details, and paragraph-level breakdown. # 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("junipr/ai-content-detector").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("junipr/ai-content-detector").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 junipr/ai-content-detector --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=junipr/ai-content-detector", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Content Detector", "description": "Detect AI-generated text using statistical analysis. Analyzes perplexity, burstiness, vocabulary diversity, repetition patterns, and writing style. Returns confidence scores, classification (AI/human/mixed), and detailed signal breakdowns. Batch processing for multiple texts.", "version": "1.0", "x-build-id": "4vxwoUMWaxKaW9lsd" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/junipr~ai-content-detector/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-junipr-ai-content-detector", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/junipr~ai-content-detector/runs": { "post": { "operationId": "runs-sync-junipr-ai-content-detector", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/junipr~ai-content-detector/run-sync": { "post": { "operationId": "run-sync-junipr-ai-content-detector", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "texts": { "title": "Texts", "type": "array", "description": "List of texts to analyze for AI content. Each can be a plain string or an object with text, id, and metadata fields.", "default": [ "The quick brown fox jumps over the lazy dog. This is a simple sentence written naturally.", "In the realm of artificial intelligence, large language models have demonstrated remarkable capabilities in generating human-like text across a wide variety of domains and applications. Furthermore, it is important to note that these systems exhibit consistent patterns." ] }, "analyzePerplexity": { "title": "Analyze Perplexity", "type": "boolean", "description": "Analyze word-level perplexity (predictability). AI text tends to have uniformly low perplexity.", "default": true }, "analyzeBurstiness": { "title": "Analyze Burstiness", "type": "boolean", "description": "Analyze sentence length variation. Human text has high burstiness; AI text is more uniform.", "default": true }, "analyzeVocabulary": { "title": "Analyze Vocabulary", "type": "boolean", "description": "Analyze vocabulary diversity (type-token ratio). AI text often has lower vocabulary diversity.", "default": true }, "analyzeRepetition": { "title": "Analyze Repetition", "type": "boolean", "description": "Detect phrase repetition patterns and filler phrases common in AI text.", "default": true }, "analyzeStyle": { "title": "Analyze Style", "type": "boolean", "description": "Analyze stylistic uniformity (paragraph length variation, punctuation patterns).", "default": true }, "paragraphLevel": { "title": "Paragraph Level Analysis", "type": "boolean", "description": "Analyze each paragraph separately to detect mixed human+AI content.", "default": true }, "aiThreshold": { "title": "AI Threshold", "type": "number", "description": "Probability threshold above which text is classified as likely AI-generated. Min: 0.50, Max: 0.95.", "default": 0.65 }, "includeSignalDetails": { "title": "Include Signal Details", "type": "boolean", "description": "Include detailed breakdown of each analysis signal.", "default": true }, "onlyAiDetected": { "title": "Only AI Detected", "type": "boolean", "description": "Only output texts classified as AI-generated.", "default": false }, "onlyHumanDetected": { "title": "Only Human Detected", "type": "boolean", "description": "Only output texts classified as human-written.", "default": false } } }, "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 } } } } } } } } } } ```