# Mentat Authenticity Audit (`mentat-logic/mentat-authenticity-audit`) Actor Audit the authenticity of Facebook comment sections from your own exports: coordination signals with evidence — timing bursts, duplicate talking points, reply chains — never verdicts. Insufficient data says so honestly. $0.66 per 1,000 unique comments; the demo is free. - **URL**: https://apify.com/mentat-logic/mentat-authenticity-audit.md - **Developed by:** [Mentat Logic](https://apify.com/mentat-logic) (community) - **Categories:** Social media - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $0.66 / 1,000 unique comment 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Engagement Authenticity Audit — Facebook Comments Feed it the comments export from your own posts or campaign — any Facebook comments scraper's output — and get back per-account **authenticity signals with evidence**: timing bursts, duplicate-text clusters, coordinated reply chains, machine-regular cadence. Never a verdict. Never a guess. Who it's for: advertisers asking "did my budget buy real engagement or a swarm?" · PR and comms teams checking whether a pile-on is organic · agencies auditing engagement quality before the client meeting. ### What it detects — and the three tiers of fake - Automated bots leak automation: machine-even timing, generated-name cohorts, mechanical cadence. - Cheap operated fakes (real humans on fake accounts, paid per comment — the most common tier in real swarms) defeat automation signals, but their coordination leaks instead: timing bursts, near-identical talking points, cross-post repetition, mutual reply-chains. - Professionally managed personas are indistinguishable from real people in engagement data by design — and the report says so, in a section titled "What this audit cannot detect." We state the limit; that is what makes the rest believable. ### The honesty rules - Every signal carries its evidence (the burst window, the shared text, the interval series). Levels: NO SIGNALS OBSERVED · WEAK · MULTIPLE · STRONG PATTERNS — plus INSUFFICIENT SIGNAL as a first-class answer (one comment is not enough to judge anyone, so we don't). - The word "bot" appears nowhere as a conclusion. Signals describe behaviour in this dataset; they never characterize persons. - Duplicates are removed before counting — and before billing. - Absent data renders as "—". Coverage (rows in, window, dedup count, dateless rows) is printed into the report, not kept in a footnote. ### What you get A dataset (summary · per-account rows with signals+evidence · bursts · duplicate clusters) and a client-ready dark HTML report report_url in the summary) with the burst timeline, cluster examples, flagged accounts, the cannot-detect section, and the full coverage block. ### Input - Nothing → free synthetic demo: a believable comment section with a planted 40-second burst, a talking-point cluster, a generated-name cohort, a mutual-reply pair, honestly messy humans — and one single-comment account that correctly returns INSUFFICIENT SIGNAL. Watch it refuse. - *datasetId** → an Apify dataset from your comments scraper run. - *items** → comment rows pasted directly. The field mapper states which mapping it used and refuses to analyze if too little of the format maps — a wrong analysis is worse than none. ### What it deliberately does NOT do - No scraping — it never contacts Facebook; it processes data you already have, and stores nothing beyond the run's own output. - No "bot scores," no percentages of fake, no certified-human badges — those numbers cannot be honest from engagement data alone. - No detection of professionally managed personas (stated above, stated in every report). ### Price The first 1,500 unique comments of every run are free — enough to audit one typical post end-to-end before paying anything. $0.66 per 1,000 unique comments analyzed — deduplicated first, so you never pay twice for the same comment. Platform compute is included; the demo run is completely free. (A 1,000-comment post ≈ $0.66; a 30,000-comment campaign ≈ $20.) ### Permissions, stated plainly This Actor requests full permissions for one reason: to read the dataset ID you point it at. It writes only to its own run storage. It never modifies, deletes, or reads anything else in your account. --- 📖 Companion reading: [The Facebook URL Zoo](https://dev.to/mentat_logic/the-facebook-url-zoo-every-page-link-format-and-how-to-canonicalize-them-4jfe) · [How to compare Facebook pages without fooling yourself](https://dev.to/mentat_logic/how-to-compare-facebook-pages-without-fooling-yourself-2l57) Mentat Logic — analysis tools that state their coverage, show their working, and render the unknown as unknown. # Actor input Schema ## `datasetId` (type: `string`): ID of an Apify dataset holding a Facebook comments-scraper export. Leave everything empty to run the free synthetic demo. ## `items` (type: `array`): Alternatively, paste comment rows directly (array of objects). ## Actor input object example ```json {} ```` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("mentat-logic/mentat-authenticity-audit").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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("mentat-logic/mentat-authenticity-audit").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 mentat-logic/mentat-authenticity-audit --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=mentat-logic/mentat-authenticity-audit", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Mentat Authenticity Audit", "description": "Audit the authenticity of Facebook comment sections from your own exports: coordination signals with evidence — timing bursts, duplicate talking points, reply chains — never verdicts. Insufficient data says so honestly. $0.66 per 1,000 unique comments; the demo is free.", "version": "0.0", "x-build-id": "uK2IjJPx7hZNnnUe0" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/mentat-logic~mentat-authenticity-audit/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-mentat-logic-mentat-authenticity-audit", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/mentat-logic~mentat-authenticity-audit/runs": { "post": { "operationId": "runs-sync-mentat-logic-mentat-authenticity-audit", "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/mentat-logic~mentat-authenticity-audit/run-sync": { "post": { "operationId": "run-sync-mentat-logic-mentat-authenticity-audit", "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": { "datasetId": { "title": "Dataset ID", "type": "string", "description": "ID of an Apify dataset holding a Facebook comments-scraper export. Leave everything empty to run the free synthetic demo." }, "items": { "title": "Raw comments (JSON)", "type": "array", "description": "Alternatively, paste comment rows directly (array of objects)." } } }, "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 } } } } } } } } } } ```