# X(Twitter) Post Quotes Scraper (`powerai/twitter-post-quotes-scraper`) Actor
Collect quote posts that reference a given X post—full tweet payloads with authors, text, and nested quoted context.
- **URL**: https://apify.com/powerai/twitter-post-quotes-scraper.md
- **Developed by:** [PowerAI](https://apify.com/powerai) (community)
- **Categories:** Social media, Videos, Integrations
- **Stats:** 3 total users, 2 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 Post Quotes Scraper
Gather **quote posts** that reference a specific **original X post**—the same kind of items people see when they open the “Quotes” view for a post. Each dataset row is one **quote tweet** (the new post that embeds the original), including text, author, engagement, optional location, and—when present—a large nested **`quoted_status_result`** subtree for the embedded original post.
### Who it’s for
- **Social & comms teams** tracking commentary on a flagship post.
- **Researchers & analysts** who need structured exports instead of scrolling the quote list.
- **Data workflows** that join quote posts with your own content or campaign ids.
### What you can do with it
- **Target one original post** by its numeric id (from the post URL).
- **Collect many quote posts** in one run until you reach the limit you set or the list ends.
- **Match every row** to that original post via **`requestedSourcePostId`**, plus **`scrapedAt`** on each row.
### How it works (in plain terms)
You enter the **original post’s numeric id** (not the `@handle`). The tool loads quote posts in successive batches until your **maximum** is reached or there are no more to load—you do not need to manage paging yourself.
### Input
| Field | Required | What it means |
|-------|----------|----------------|
| **Original post ID** (`tweetId`) | Yes | Which post’s quote posts you want to export. |
| **Maximum results** (`maxResults`) | No | Upper limit on how many quote-post rows to collect (default **50**). |
### Output
- **One row per quote post** (until your cap). Rows are often **very large** because the embedded original post is included in full.
- **Post identity & stats**: **`__typename`** (`Tweet`), **`rest_id`**, posting **`source`**, **`views`** (**`count`** and **`state`** when shown), **`edit_control`**, **`grok_analysis_button`**, **`is_translatable`**, **`unmention_data`**.
- **Author**: **`core.user_results.result`** is a **`User`** with **`avatar`**, **`core`** (name/handle), **`legacy`** profile fields, optional **`professional`** (creator categories), **`dm_permissions`**, **`media_permissions`**, **`relationship_perspectives`**, **`verification`**, **`tipjar_settings`**, etc.
- **Quote post body**: **`legacy`** includes **`full_text`**, **`created_at`**, **`conversation_id_str`**, **`display_text_range`**, **`entities`**, **`bookmark_count`**, engagement counters, **`lang`**, **`is_quote_status`**, **`quoted_status_id_str`**, **`quoted_status_permalink`**, **`user_id_str`**, and optionally **`place`** (geo polygon, name, country) when the quote post is geotagged.
- **Embedded original**: **`quoted_status_result.result`** is typically another full **`Tweet`** (same general shape—**`core`**, **`legacy`**, media in **`extended_entities`**, **`views`**, etc.). This block alone can be hundreds of lines.
- **Export metadata**: **`requestedSourcePostId`** repeats the original post id you asked for; **`scrapedAt`** is when that row was saved.
Optional fields vary by post. Details may change as X updates its product.
#### Sample output (one dataset row, heavily trimmed)
The JSON below keeps real ids and text from a typical row but omits most of **`quoted_status_result`** (the embedded original) and shortens **`core.user_results`**.
```json
{
"__typename": "Tweet",
"core": {
"user_results": {
"result": {
"__typename": "User",
"rest_id": "285493427",
"core": {
"name": "Makhareezy",
"screen_name": "Makharanga"
},
"legacy": {
"description": "Success is power through God••…"
},
"professional": {
"professional_type": "Creator",
"category": [{ "name": "Podcast", "id": 979 }]
}
}
}
},
"edit_control": {
"edit_tweet_ids": ["1623502970271801350"],
"is_edit_eligible": true,
"edits_remaining": "5"
},
"legacy": {
"full_text": "History made ... What's crazy is about 10% of the world does watch his videos",
"created_at": "Thu Feb 09 02:04:05 +0000 2023",
"conversation_id_str": "1623502970271801350",
"is_quote_status": true,
"quoted_status_id_str": "1552735248026411010",
"quoted_status_permalink": {
"display": "x.com/MrBeast/status…",
"expanded": "https://twitter.com/MrBeast/status/1552735248026411010",
"url": "https://t.co/PkizCMdfCk"
},
"favorite_count": 0,
"retweet_count": 0,
"reply_count": 0,
"quote_count": 0
},
"quoted_status_result": {
"result": {
"__typename": "Tweet",
"rest_id": "1552735248026411010",
"legacy": {
"full_text": "Over a million people celebrated 100,000,000 subscribers with me 🥹…"
}
}
},
"rest_id": "1623502970271801350",
"source": "Twitter for Android",
"views": {
"count": "36",
"state": "EnabledWithCount"
},
"requestedSourcePostId": "1552735248026411010",
"scrapedAt": "2026-03-26T02:23:32.336Z"
}
````
### Good to know
- **Private, deleted, or limited posts** may return few or no quote rows.
- Respect X’s rules and applicable laws when using exported data.
# Actor input Schema
## `tweetId` (type: `string`):
The numeric id of the post you want quote posts for (the post that others are quoting in the quote-post view).
## `maxResults` (type: `integer`):
How many quote-post rows to save in total.
## Actor input object example
```json
{
"tweetId": "1552735248026411010",
"maxResults": 10
}
```
# 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 = {
"tweetId": "1552735248026411010",
"maxResults": 10
};
// Run the Actor and wait for it to finish
const run = await client.actor("powerai/twitter-post-quotes-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 = {
"tweetId": "1552735248026411010",
"maxResults": 10,
}
# Run the Actor and wait for it to finish
run = client.actor("powerai/twitter-post-quotes-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 '{
"tweetId": "1552735248026411010",
"maxResults": 10
}' |
apify call powerai/twitter-post-quotes-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=powerai/twitter-post-quotes-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "X(Twitter) Post Quotes Scraper",
"description": "Collect quote posts that reference a given X post—full tweet payloads with authors, text, and nested quoted context.",
"version": "0.0",
"x-build-id": "f6GMXcLNv4GZESexf"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/powerai~twitter-post-quotes-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-powerai-twitter-post-quotes-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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/powerai~twitter-post-quotes-scraper/runs": {
"post": {
"operationId": "runs-sync-powerai-twitter-post-quotes-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/powerai~twitter-post-quotes-scraper/run-sync": {
"post": {
"operationId": "run-sync-powerai-twitter-post-quotes-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": [
"tweetId"
],
"properties": {
"tweetId": {
"title": "Original post ID",
"type": "string",
"description": "The numeric id of the post you want quote posts for (the post that others are quoting in the quote-post view).",
"default": "1552735248026411010"
},
"maxResults": {
"title": "Maximum results",
"minimum": 20,
"maximum": 10000,
"type": "integer",
"description": "How many quote-post rows to save in total.",
"default": 10
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```