# Meta Threads Profile Scraper (`solid-scraper/meta-threads-profile-scraper`) Actor 📌 Meta Threads Profile Scraper extracts public profile data fast and accurately for leads, research, and marketing. 🔍 Target by keywords and segment insights instantly—ideal for growth teams and agencies. 🚀 Save time, boost outreach. - **URL**: https://apify.com/solid-scraper/meta-threads-profile-scraper.md - **Developed by:** [SolidScraper](https://apify.com/solid-scraper) (community) - **Categories:** Lead generation, Social media, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.99 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ### Meta Threads Profile Scraper 🔍 **Meta Threads Profile Scraper** is a purpose-built tool that extracts comprehensive profile metadata and the latest posts from Threads.net profiles. Whether you’re using a Threads profile scraper for lead research, building Threads profile analytics, or simply scraping Threads usernames for outreach workflows, this actor helps you automate the busy work—at scale—using publicly available data. It’s designed for marketers, researchers, and data analysts who want structured Threads user profile data in minutes, saving you hours of manual work. --- ### Why choose Meta Threads Profile Scraper? | Feature | Benefit | |---|---| | ✅ All-in-one Threads profile extraction | Pull profile metadata and `latestPosts` in a single run for faster analysis | | ✅ Profile + latest posts in structured JSON | Get clean, consistent data fields ready for downstream processing | | ✅ Resilient scraping approach | Uses built-in proxy support for reliable scraping and resilience | | ✅ Designed for batch runs | Scrape multiple Threads accounts by listing `usernames` once | | ✅ Automation-friendly output | Produces a predictable JSON structure you can export and reuse (JSON/CSV via Apify) | | ✅ Clear failure states | Returns an `error` field for profiles that can’t be processed | --- ### Key features - 📊 **Comprehensive profile metadata**: Extracts fields like `is_private`, `follower_count`, `is_verified`, `biography`, `full_name`, and more. - 🆔 **Threads public profile scraper for usernames**: Accepts an array of Threads usernames so you can scrape multiple accounts in one go. - 📝 **Latest posts included**: Returns a `latestPosts` list formatted into the output schema’s post fields. - 🔄 **Resilience built for reliability**: Includes proxy support and robust handling so runs keep moving through profiles. - 💾 **Structured data for easy analysis**: Outputs consistent JSON per profile, making it simple to load into spreadsheets, BI tools, or ETL pipelines. - 🌐 **Built-in URL normalization**: Produces the profile `url` for each username automatically in the output. --- ### Input Provide input via an `input.json` file. Example structure: ```json { "usernames": [ "guinnessworldrecords" ] } ```` #### Input Fields | Field | Required | Description | |---|---:|---| | `usernames` | Yes | List of Threads usernames to scrape. Example: `guinnessworldrecords`. | *** ### Output The actor saves each profile’s data in JSON format, pushed as a single item containing a `result` array of all scraped profiles. Example output structure: ```json { "result": [ { "url": "https://www.threads.net/@exampleuser", "is_private": false, "profile_pic_url": "https://example.com/pic.jpg", "friendship_status": "SOME_STATUS", "has_onboarded_to_text_post_app": true, "pk": 123456, "text_post_app_is_private": false, "username": "exampleuser", "text_post_app_remove_mention_entrypoint": "SOME_VALUE", "text_app_custom_feeds": "SOME_VALUE", "gating": "SOME_VALUE", "follower_count": 1234, "profile_context_facepile_users": [], "hd_profile_pic_versions": [], "text_post_app_public_views": 5678, "is_verified": false, "biography": "Example biography text", "text_app_biography": "Example text app biography", "full_name": "Example Full Name", "bio_links": [], "profile_tags": [], "transparency_label": "SOME_LABEL", "show_text_post_app_badge": true, "platform_podcast_info": null, "platform_podcast_episode_info": null, "id": "SOME_ID", "latestPosts": [ { "id": "POST_ID", "logging_info_token": "LOG_TOKEN", "pk": 98765, "user": { "username": "exampleuser" }, "text_post_app_info": { "SOME": "VALUE" }, "is_paid_partnership": false, "audio": null, "caption": "Example caption", "caption_is_edited": false, "transcription_data": null, "carousel_media": [], "code": null, "image_versions2": null, "original_height": 1080, "original_width": 1080, "accessibility_caption": null, "usertags": [], "video_versions": [], "has_audio": false, "media_type": 1, "caption_add_on": null, "like_count": 42, "giphy_media_info": null, "prototyping_only_glimmer_post_info": null, "media_overlay_info": null, "metaPlace": null, "meta_place": null, "gen_ai_detection_method": null, "taken_at": 1710000000, "organic_tracking_token": null, "__token": null, "canonical_url": "https://www.threads.net/@exampleuser/POST_ID", "like_and_view_counts_disabled": false } ] } ] } ``` > Note: For profiles that can’t be processed, the actor returns an object containing `error` and `username` instead of the profile fields. #### Output Fields | Field | Type | Description | |---|---|---| | `url` | string | Normalized Threads profile URL for the scraped account | | `is_private` | boolean | Whether the profile is marked as private | | `profile_pic_url` | string | URL of the profile picture | | `friendship_status` | any | Current relationship/friendship status from the profile data | | `has_onboarded_to_text_post_app` | boolean | Whether the user has onboarded to the text posting experience | | `pk` | any | Profile primary key from the profile data | | `text_post_app_is_private` | boolean | Privacy state specifically for the text posting experience | | `username` | string | The username used for scraping | | `text_post_app_remove_mention_entrypoint` | any | Mention-related entrypoint value from the profile data | | `text_app_custom_feeds` | any | Custom feeds configuration from the profile data | | `gating` | any | Gating information from the profile data | | `follower_count` | number | Number of followers | | `profile_context_facepile_users` | array | Facepile users shown in the profile context | | `hd_profile_pic_versions` | array | Higher-definition profile picture versions | | `text_post_app_public_views` | any | Public view metric from the profile data | | `is_verified` | boolean | Whether the profile is verified | | `biography` | string | Main profile biography text | | `text_app_biography` | string | Biography text from the text app context | | `full_name` | string | Full name shown on the profile | | `bio_links` | array | Links listed in the bio | | `profile_tags` | array | Tags associated with the profile | | `transparency_label` | any | Transparency label from the profile data | | `show_text_post_app_badge` | boolean | Whether the text post app badge is shown | | `platform_podcast_info` | any | Podcast info (if present) | | `platform_podcast_episode_info` | any | Podcast episode info (if present) | | `id` | any | Profile id field from the profile data | | `latestPosts` | array | List of latest post objects formatted into the post schema below | | `error` | string | Error details when scraping fails for a username | | `username` | string | Username associated with the error | **`latestPosts` post fields (returned inside `latestPosts`)** include: `id`, `logging_info_token`, `pk`, `user`, `text_post_app_info`, `is_paid_partnership`, `audio`, `caption`, `caption_is_edited`, `transcription_data`, `carousel_media`, `code`, `image_versions2`, `original_height`, `original_width`, `accessibility_caption`, `usertags`, `video_versions`, `has_audio`, `media_type`, `caption_add_on`, `like_count`, `giphy_media_info`, `prototyping_only_glimmer_post_info`, `media_overlay_info`, `metaPlace`, `meta_place`, `gen_ai_detection_method`, `taken_at`, `organic_tracking_token`, `__token`, `canonical_url`, `like_and_view_counts_disabled`. *** ### How to use Meta Threads Profile Scraper (via Apify Console) 1. **Open Apify Console**\ Go to [console.apify.com](https://console.apify.com) and sign in. 2. **Find the actor page**\ Search for **Meta Threads Profile Scraper** and open the actor details page. 3. **Go to the INPUT section**\ Paste your input JSON into the input editor. This actor expects a `usernames` array. 4. **Add one or more Threads usernames**\ Enter usernames (e.g., `guinnessworldrecords`) under `usernames`. This supports scraping multiple Threads accounts in one run. 5. **(Optional) Review proxy settings**\ The run uses proxy support for reliable scraping. If you’re running large batches, you can rely on the actor’s built-in resilience. 6. **Start the run**\ Click **Run**. Watch the logs to see progress as each profile is scraped and processed. 7. **Wait for results**\ When finished, open the run’s **OUTPUT** / dataset results. You’ll receive a JSON structure containing `result` with an array of profile objects (each with `latestPosts`). 8. **Export for analysis**\ Export the dataset to your preferred format (for example, JSON and CSV are commonly available in Apify exports) and load it into your CRM, spreadsheet, or analytics workflow. No coding required—get accurate **Meta Threads Profile Scraper** results in minutes. 🚀 *** ### Advanced features & SEO optimization - 🔧 **Engineered for Meta Threads profile data extraction**: Specifically built to excel at “Threads profile scraper” workflows where you need profile fields plus `latestPosts`. - 🔍 **Keyword-driven profile research**: Supports scraping Threads usernames for automated Threads user profile scraper pipelines (for example, building a structured lead list). - 🛡️ **Reliability-first scraping**: Uses built-in proxy support and includes resilience so runs handle real-world variability better. - 💾 **Structured JSON output**: Produces a stable schema for Threads public profile scraper use cases—ideal for Threads profile analytics scraper projects. - 🌐 **Public-facing data collection**: Collects publicly available data and returns it in a clean, analysis-ready format (with clear error outputs when something can’t be extracted). *** ### Best use cases - 📈 **Growth marketing lead research**: Automate collection of Threads profile metadata and latest posts to enrich lead lists at scale. - 🕵️ **Competitive profiling**: Compare multiple Threads account profiles quickly using consistent fields like `follower_count`, `is_verified`, and `biography`. - 🎓 **Academic or UX research**: Build datasets for Threads profile analytics by capturing structured profile context and post content metadata. - 🧾 **Data analysis & reporting**: Feed the JSON output into notebooks or dashboards to explore engagement signals like `like_count` over recent posts. - 🤝 **Community management**: Track public-facing profile attributes across many accounts for outreach and relationship workflows. - 🛠️ **Automation pipelines (API or ETL-like workflows)**: Integrate the actor output into your data processing stack as part of Meta Threads API scraping-style processes. - 📊 **Influencer discovery**: Use Threads profile scraper outputs to evaluate accounts for collaboration based on verified status, follower counts, and bios. *** ### Technical specifications - **Supported Input Formats** - ✅ `usernames`: array of Threads usernames (e.g., `"guinnessworldrecords"`) - **Proxy Support** - ✅ Built-in proxy support for reliable scraping - **Retry Mechanism** - ❗ Retries/fallback behavior is handled internally; the actor is designed to keep runs resilient across profiles - **Dataset Structure** - ✅ Actor pushes a single item containing: - `result`: array of per-username objects (profile data or error objects) - **Rate Limits & Performance** - ✅ Processing includes pacing between profiles (small delay) to reduce the chance of rate limiting - ✅ Performance varies by profile accessibility and network conditions - **Limitations** - ❌ If the actor cannot find profile data in the returned HTML, it returns an `error` for that `username` - ❌ Some posts/profile fields may be missing depending on what’s available publicly for a given account *** ### FAQ #### What does Meta Threads Profile Scraper extract from each account? ✅ It extracts profile metadata such as `is_private`, `follower_count`, `is_verified`, `biography`, `full_name`, `bio_links`, and more, and it also includes a `latestPosts` array formatted with many post-level fields. #### Can I scrape multiple Threads accounts in one run? ✅ Yes. You provide a list of Threads usernames in the `usernames` input field, and the actor processes them one by one, returning results for each. #### What does the output look like if a profile fails? ❌ If the actor cannot process a username successfully, it returns an object containing `error` and `username` for that entry (instead of the full profile fields). #### Is authentication or login required? ❌ The actor is designed to extract publicly available data from Threads.net profiles and does not require user authentication via the input. #### How do I get the results after the run completes? ✅ After the actor finishes, open the run’s dataset/output in Apify Console. The actor pushes a single item containing `result`, which is an array of all scraped profile objects. #### Can I export the data to tools like spreadsheets or BI dashboards? ✅ Yes. Since the actor outputs structured JSON, you can export the dataset from Apify to formats such as CSV/JSON (depending on your Apify setup) and then load it into your tools. #### Does this help with automated Threads profile scraping for analytics? ✅ Absolutely. The returned `latestPosts` and profile metadata fields make it suitable for building datasets for Threads profile analytics scraper workflows and other automated Threads profile scraping software projects. *** ### Support & feature requests If you’re using **Meta Threads Profile Scraper** and want improvements, tell us what would make your Threads profile scraping workflow faster or more reliable. 😊 - 💡 **Feature Requests**: For example, you could ask for enhancements like additional export formats or extra normalization for specific fields used in Threads profile data scraper pipelines. - 📧 **Contact**: Email us at . Your feedback helps shape the roadmap for better Meta Threads profile extraction. 🚀 *** *If you’re looking for the most comprehensive Meta Threads Profile Scraper for structured profile metadata and latest posts, this actor is built to get you there fast and reliably.* # Actor input Schema ## `usernames` (type: `array`): List of Threads usernames to scrape. ## Actor input object example ```json { "usernames": [ "guinnessworldrecords" ] } ``` # 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 = { "usernames": [ "guinnessworldrecords" ] }; // Run the Actor and wait for it to finish const run = await client.actor("solid-scraper/meta-threads-profile-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 = { "usernames": ["guinnessworldrecords"] } # Run the Actor and wait for it to finish run = client.actor("solid-scraper/meta-threads-profile-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 '{ "usernames": [ "guinnessworldrecords" ] }' | apify call solid-scraper/meta-threads-profile-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=solid-scraper/meta-threads-profile-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Meta Threads Profile Scraper", "description": "📌 Meta Threads Profile Scraper extracts public profile data fast and accurately for leads, research, and marketing. 🔍 Target by keywords and segment insights instantly—ideal for growth teams and agencies. 🚀 Save time, boost outreach.", "version": "0.1", "x-build-id": "7jS5KKjnBio4JjYvy" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/solid-scraper~meta-threads-profile-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-solid-scraper-meta-threads-profile-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/solid-scraper~meta-threads-profile-scraper/runs": { "post": { "operationId": "runs-sync-solid-scraper-meta-threads-profile-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/solid-scraper~meta-threads-profile-scraper/run-sync": { "post": { "operationId": "run-sync-solid-scraper-meta-threads-profile-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "usernames" ], "properties": { "usernames": { "title": "Usernames", "type": "array", "description": "List of Threads usernames to scrape.", "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 } } } } } } } } } } ```