# X(Twitter) Community Posts Scraper (`powerai/twitter-community-tweets-scraper`) Actor
Collect posts from an X Community feed—text, media, and engagement—into structured rows for research or reporting.
- **URL**: https://apify.com/powerai/twitter-community-tweets-scraper.md
- **Developed by:** [PowerAI](https://apify.com/powerai) (community)
- **Categories:** Social media, Videos, Integrations
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks
- **User rating**: No ratings yet
## Pricing
from $4.99 / 1,000 results
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
## X Community Posts Scraper
Gather **posts from an X (Twitter) Community**—the same kinds of items people see in that Community’s feed—into dataset rows. Each row is one post with nested text, media, author, Community context, and engagement details when available.
### Who it’s for
- **Community managers** reviewing what is being shared in a space they run.
- **Researchers & analysts** who need exports instead of manual scrolling.
- **Social & comms** teams tracking conversations around a topic or brand Community.
### What you can do with it
- **Collect many posts** in one run until you reach the limit you set or there is nothing more to load.
- **Choose how the feed is presented** (standard stream vs. one that emphasizes photos and video).
- **Optionally influence ordering** when the product exposes sort options (relevance, newest, or likes).
- **Match rows to your inputs** via **`communityId`**, **`searchType`**, and optional **`rankingMode`** on each row, plus **`scrapedAt`**.
### How it works (in plain terms)
You enter the **Community’s numeric id** (the same id you see in the Community’s URL or similar places). You also pick **which feed view** to use and, if you want, a **sort order**. The tool loads posts in successive batches until your **maximum** is reached or the feed ends—you do not need to manage paging yourself.
### Input
| Field | Required | What it means |
|-------|----------|----------------|
| **Community ID** (`communityId`) | Yes | Which Community’s posts to export. |
| **Feed type** (`searchType`) | Yes | Regular community stream (**Default**) or a media-focused stream (**Media**). |
| **Sort order** (`rankingMode`) | No | When available: **Relevance**, **Recency**, or **Likes**. |
| **Maximum results** (`maxResults`) | No | Upper limit on how many post rows to collect (default **50**). |
### Output
Each dataset row combines the **post payload** with a few **export fields** you chose at run time.
- **Post identity & surface fields**: **`__typename`**, **`rest_id`**, posting **`source`**, **`views`** (count and state when shown), **`edit_control`** when edits apply, and flags such as **`is_translatable`** when present.
- **Community context (author)**: **`author_community_relationship`** bundles the poster’s relationship to the Community (**`role`**, e.g. Member), the author under **`user_results`**, and a rich **`community_results`** snapshot—Community **name**, **description**, **member_count**, **rules**, banner imagery, **members_facepile_results**, admin/creator highlights, join policies, and similar nested details when returned.
- **Lighter Community pointers**: **`community_relationship`** (e.g. moderation/pin-related hints) and **`community_results`** with **`id_str`** and viewer relationship blocks.
- **Author (standard tweet core)**: **`core.user_results`** with profile **`legacy`** fields (**`screen_name`**, **`name`**, avatars, counts, **`entities`**, etc.).
- **Content & engagement**: **`legacy`** holds **`full_text`**, **`created_at`**, **`conversation_id_str`**, **`entities`** / **`extended_entities`** (photos, videos with **`video_info.variants`**, etc.), and counters such as favorites, reposts, replies, quotes, bookmarks; plus **`is_quote_status`**, **`quoted_status_id_str`**, and **`quoted_status_permalink`** when the post quotes another.
- **Quoted post body**: when present, **`quoted_status_result.result`** repeats a full nested post (same general shape—**`core`**, **`legacy`**, **`views`**, optional Community **`community_results`**, and further nesting such as another quoted tweet inside).
- **Export metadata** (added per row): **`communityId`**, **`searchType`**, optional **`rankingMode`**, **`scrapedAt`**.
Rows are often **very large**; field names and nesting mirror what X returns for Community timelines and may change over time.
#### Sample output (one dataset row, heavily trimmed)
The snippet below keeps real field names from a typical row but omits most nested objects and long strings.
```json
{
"__typename": "Tweet",
"rest_id": "2036575486533575164",
"author_community_relationship": {
"community_results": {
"result": {
"__typename": "Community",
"name": "Generative AI",
"description": "Share AI-generated creations, news, tips and more.",
"member_count": 245906,
"id_str": "1601841656147345410"
}
},
"role": "Member",
"user_results": {
"result": {
"__typename": "User",
"rest_id": "2030621483081617408",
"legacy": {
"screen_name": "Magical_idol8",
"name": "まほアカプレミア"
}
}
}
},
"community_relationship": {
"rest_id": "2036575486533575164"
},
"community_results": {
"result": {
"__typename": "Community",
"id_str": "1601841656147345410"
}
},
"core": {
"user_results": {
"result": {
"__typename": "User",
"rest_id": "2030621483081617408",
"legacy": {
"screen_name": "Magical_idol8",
"name": "まほアカプレミア"
}
}
}
},
"edit_control": {
"edit_tweet_ids": ["2036575486533575164"],
"is_edit_eligible": false
},
"legacy": {
"created_at": "Tue Mar 24 22:46:48 +0000 2026",
"full_text": "おはようございます！…",
"conversation_id_str": "2036575486533575164",
"favorite_count": 104,
"retweet_count": 49,
"reply_count": 43,
"quote_count": 8,
"is_quote_status": true,
"quoted_status_id_str": "2036214605626875983"
},
"quoted_status_result": {
"result": {
"__typename": "Tweet",
"rest_id": "2036214605626875983",
"legacy": {
"full_text": "おはようございます！…",
"favorite_count": 222
}
}
},
"source": "Twitter Web App",
"views": {
"count": "8287",
"state": "EnabledWithCount"
},
"communityId": "1601841656147345410",
"searchType": "Default",
"rankingMode": "Relevance",
"scrapedAt": "2026-03-25T09:15:09.894Z"
}
````
### Good to know
- The **Community id** must be correct; private or restricted Communities may return few or no rows.
- **Pinned** content may also appear in the main list—duplicates are reduced when possible, but always validate in your own pipeline if you need strict uniqueness.
- Respect X’s rules and applicable laws when using exported data.
# Actor input Schema
## `communityId` (type: `string`):
The numeric id of the X Community whose posts you want to collect.
## `searchType` (type: `string`):
Which view of the Community feed to use: the regular stream, or one that prioritizes posts with photos or video.
## `rankingMode` (type: `string`):
Optional. How to order posts when the product supports it—by relevance, newest first, or most liked.
## `maxResults` (type: `integer`):
How many post rows to save in total.
## Actor input object example
```json
{
"communityId": "1601841656147345410",
"searchType": "Default",
"maxResults": 18
}
```
# 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 = {
"communityId": "1601841656147345410",
"searchType": "Default",
"maxResults": 18
};
// Run the Actor and wait for it to finish
const run = await client.actor("powerai/twitter-community-tweets-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 = {
"communityId": "1601841656147345410",
"searchType": "Default",
"maxResults": 18,
}
# Run the Actor and wait for it to finish
run = client.actor("powerai/twitter-community-tweets-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 '{
"communityId": "1601841656147345410",
"searchType": "Default",
"maxResults": 18
}' |
apify call powerai/twitter-community-tweets-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=powerai/twitter-community-tweets-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "X(Twitter) Community Posts Scraper",
"description": "Collect posts from an X Community feed—text, media, and engagement—into structured rows for research or reporting.",
"version": "0.0",
"x-build-id": "aic6xvA1B4610PaGt"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/powerai~twitter-community-tweets-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-powerai-twitter-community-tweets-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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/powerai~twitter-community-tweets-scraper/runs": {
"post": {
"operationId": "runs-sync-powerai-twitter-community-tweets-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/powerai~twitter-community-tweets-scraper/run-sync": {
"post": {
"operationId": "run-sync-powerai-twitter-community-tweets-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": [
"communityId",
"searchType"
],
"properties": {
"communityId": {
"title": "Community ID",
"type": "string",
"description": "The numeric id of the X Community whose posts you want to collect.",
"default": "1601841656147345410"
},
"searchType": {
"title": "Feed type",
"enum": [
"Default",
"Media"
],
"type": "string",
"description": "Which view of the Community feed to use: the regular stream, or one that prioritizes posts with photos or video.",
"default": "Default"
},
"rankingMode": {
"title": "Sort order",
"enum": [
"Relevance",
"Recency",
"Likes"
],
"type": "string",
"description": "Optional. How to order posts when the product supports it—by relevance, newest first, or most liked."
},
"maxResults": {
"title": "Maximum results",
"minimum": 18,
"maximum": 50000,
"type": "integer",
"description": "How many post rows to save in total.",
"default": 18
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```