# Telegram Like Scraper (`i-scraper/telegram-like-scraper`) Actor ⚡️ Telegram Like Scraper: Extract likes from posts from Telegram channels. - **URL**: https://apify.com/i-scraper/telegram-like-scraper.md - **Developed by:** [i-Scraper](https://apify.com/i-scraper) (community) - **Categories:** Social media - **Stats:** 28 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 one post statistics 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 ## Telegram Like Scraper > 📊 Powerful Apify Actor for extracting **like statistics, reactions, and view counts** from Telegram channel messages. **Telegram Like Scraper** automatically downloads engagement metrics (likes, emoji reactions, views) from channel messages you specify, tracking all reaction types including custom emojis and paid Star reactions. --- ### 1. Key Features & Use Cases * 📊 **Complete Engagement Analytics** — track all reaction types (❤️ 👍 🔥 😍 🎉), view counts, and Star reactions to measure content performance and audience sentiment. * 🎯 **Competitor Research** — compare reaction patterns across multiple channels to benchmark your content strategy against competitors. * � **Content Optimization** — identify which posts generate the most engagement by analyzing reaction-to-view ratios and emoji sentiment. * � **Market Research** — collect reaction data from thousands of messages for sentiment analysis, trend detection, and audience behavior studies. * 💎 **Premium Features** — access custom emoji reactions and paid Star reactions leaderboard to understand super-fan engagement. * ⚡ **Enterprise-Ready** — process multiple channels concurrently with automatic rate limiting, retry logic, and session management. --- ### 2. Input configuration #### Example Configuration ```json { "channels": [ "@durov", "https://t.me/telegram" ], "historyFrom": "2025-01-01T00:00:00Z", "historyTo": "2025-01-31T23:59:59Z", "limit": 1000, "telegramSession": "${{ TELEGRAM_SESSION }}", "telegram2faPassword": "${{ TELEGRAM_2FA_PASSWORD }}" } ```` Parameters explained: - `channels` *(array, required)* — Telegram channel identifiers accepted in any of the following forms: - `@channel` - `https://t.me/channel` *(public)* - Channel username without @ symbol - `historyFrom` *(string, required)* — ISO-8601 timestamp. Messages **older** than this value are skipped. - `historyTo` *(string, optional)* — ISO-8601 timestamp. Messages **newer** than this value are skipped. Defaults to current date. - `limit` *(integer, optional, default **1000**)* — maximum number of messages to fetch **per channel**. - `telegramSession` *(string, optional)* — pass a session string directly instead of using the secret. - `telegram2faPassword` *(string, optional)* — account password if two-factor authentication is enabled. *** ### 3. Running the Actor 1. Open **Telegram Like Scraper** on Apify. 2. Add one or more channels to scrape. 3. Set the date range (`historyFrom` and optionally `historyTo`). 4. Click **Run** and open the **Logs** tab. 5. If you didn't provide a session string, a QR code will appear. In the Telegram app open **Settings → Devices → Link Desktop Device** and scan the QR code. 6. If Telegram prompts for a password after scanning the QR (two-factor authentication), stop the run, open the input section and fill **Telegram 2FA password** (or set the secret `TELEGRAM_2FA_PASSWORD`) and start the Actor again. 7. (Optional) Copy the session string shown in the logs and store it as `TELEGRAM_SESSION` for future runs. *** ### 4. Output dataset The actor produces message records with like and reaction statistics in the **default dataset**: #### Message Record ```json { "channel": "durov", "message_id": 123, "date": "2025-01-15T14:23:10+00:00", "text": "Check out this amazing feature!", "views": 15420, "reactions": { "total_reactions": 342, "can_see_list": false, "reactions_as_tags": false, "reactions": [ { "type": "emoji", "emoji": "❤️", "count": 150 }, { "type": "emoji", "emoji": "👍", "count": 89 }, { "type": "emoji", "emoji": "🔥", "count": 67 }, { "type": "custom_emoji", "emoji": "5368324170671202286", "count": 23 }, { "type": "paid", "emoji": null, "count": 13 } ], "top_reactors": [ { "peer_id": null, "count": 5, "is_top": true, "anonymous": true } ] } } ``` **Field descriptions:** - `channel` — channel username - `message_id` — unique message identifier - `date` — message publication date in ISO-8601 format - `text` — message text content (can be null for media-only posts) - `views` — number of views the message received - `reactions.total_reactions` — sum of all reaction counts - `reactions.reactions` — array of reaction objects with type, emoji/document\_id, and count - `reactions.top_reactors` — leaderboard of top reactors (for paid Star reactions) Download results in **JSON**, **CSV**, **Parquet** and more via Apify API or UI. *** ### 5. FAQ **Q: What types of reactions can this scraper track?**\ A: The scraper tracks three types of reactions: - **Emoji reactions** (❤️, 👍, 🔥, 😍, 🎉, etc.) - **Custom emoji reactions** (premium channel custom emojis, returned as document IDs) - **Paid reactions** (Star-based reactions with top reactors leaderboard) **Q: Can I see who left each reaction?**\ A: No. Due to Telegram's privacy protection, reaction authors are anonymous in channels. The scraper only collects aggregate statistics (counts and types), not individual user identities. **Q: I see FloodWaitError in logs — did the run fail?**\ A: No. The actor automatically waits the required time and resumes. You will only see a warning. **Q: Can I scrape reactions from private channels?**\ A: Yes, but your Telegram account must have access to the channel. Provide a valid session string for an account that is a member of the channel. **Q: Does the `limit` apply per channel or globally?**\ A: The `limit` is **per channel** — each channel specified in the `channels` array can fetch up to `limit` messages. **Q: What if a message has no reactions?**\ A: The message record will still be saved with `reactions: null` or an empty reactions object. **Q: Is it safe to provide `telegramSession` and `telegram2faPassword`?**\ A: Both values are stored in Apify **Secrets**; they never appear in logs and cannot be read by other actors. A session string only grants access to act as your account, and the 2FA password is used once during the sign-in flow. Recommended practices:\ • keep both values exclusively in Secrets;\ • regenerate / update the session string whenever you change your Telegram password;\ • you can revoke access at any moment in Telegram settings by logging out the linked device. **Q: What is the difference between view count and reaction count?**\ A: **Views** show how many times the message was viewed (reach metric). **Reactions** show how many users engaged with the message by leaving a like or emoji reaction (engagement metric). Views are typically much higher than reactions. *** ### 6. Related Telegram Scrapers Looking for more Telegram data extraction tools? Check out our complete suite: - 🔗 **[Telegram Channels Scraper](https://apify.com/i-scraper/telegram-channels-scraper)** — extract complete message history from public Telegram channels. - 💬 **[Telegram Comments Scraper](https://apify.com/i-scraper/telegram-comments-scraper)** — download comments from channel posts with flexible filtering options. - 👥 **[Telegram Groups Scraper](https://apify.com/i-scraper/telegram-groups-scraper)** — scrape message history from Telegram groups (including private invite links). *** ### 7. Support & contributions Found a bug or have a feature request? Feel free to open an issue or submit a pull request. We're happy to discuss ideas like additional metrics, filters or export formats. *** ⭐️ Launch **Telegram Like Scraper** today and turn Telegram engagement metrics into valuable data for analytics, sentiment analysis and research! # Actor input Schema ## `channels` (type: `array`): Telegram channel usernames or links to scrape likes and reactions from. ## `historyFrom` (type: `string`): Start date for message scraping (inclusive). ## `historyTo` (type: `string`): End date for message scraping (inclusive). Leave empty for current date. ## `limit` (type: `integer`): Maximum number of messages to fetch per channel. Leave empty for no limit. ## `telegramSession` (type: `string`): Optional session string to skip QR login. ## `telegram2faPassword` (type: `string`): Password for accounts with 2FA enabled. ## Actor input object example ```json { "channels": [ "@durov" ], "historyFrom": "2025-01-01", "limit": 1000 } ``` # Actor output Schema ## `messagesWithReactions` (type: `string`): Dataset containing Telegram channel messages with view counts, reaction breakdowns (emoji, custom emoji, paid), and top reactor information. # 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 = { "channels": [ "@durov" ], "historyFrom": "2025-01-01", "limit": 1000 }; // Run the Actor and wait for it to finish const run = await client.actor("i-scraper/telegram-like-scraper").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 = { "channels": ["@durov"], "historyFrom": "2025-01-01", "limit": 1000, } # Run the Actor and wait for it to finish run = client.actor("i-scraper/telegram-like-scraper").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 '{ "channels": [ "@durov" ], "historyFrom": "2025-01-01", "limit": 1000 }' | apify call i-scraper/telegram-like-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=i-scraper/telegram-like-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Telegram Like Scraper", "description": "⚡️ Telegram Like Scraper: Extract likes from posts from Telegram channels.", "version": "0.1", "x-build-id": "FwsbtJ1XIJkzuoFvD" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/i-scraper~telegram-like-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-i-scraper-telegram-like-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/i-scraper~telegram-like-scraper/runs": { "post": { "operationId": "runs-sync-i-scraper-telegram-like-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/i-scraper~telegram-like-scraper/run-sync": { "post": { "operationId": "run-sync-i-scraper-telegram-like-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "channels" ], "properties": { "channels": { "title": "Channels", "type": "array", "description": "Telegram channel usernames or links to scrape likes and reactions from.", "items": { "type": "string" } }, "historyFrom": { "title": "History from (ISO 8601)", "type": "string", "description": "Start date for message scraping (inclusive)." }, "historyTo": { "title": "History to (ISO 8601)", "type": "string", "description": "End date for message scraping (inclusive). Leave empty for current date." }, "limit": { "title": "Message limit per channel", "minimum": 1, "type": "integer", "description": "Maximum number of messages to fetch per channel. Leave empty for no limit." }, "telegramSession": { "title": "Telegram session string", "type": "string", "description": "Optional session string to skip QR login." }, "telegram2faPassword": { "title": "Telegram 2FA password", "type": "string", "description": "Password for accounts with 2FA enabled." } } }, "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 } } } } } } } } } } ```