# ๐ฆ Twitter / X User Tweet Replies Scraper (`ethereal_wool/twitter-user-tweet-replies`) Actor
Extract Twitter / X user tweet replies data โ text, author, views, and more. Scrape by keyword, URL or ID. Export to JSON, CSV & Excel, use the API, schedule runs and integrate. No code required.
- **URL**: https://apify.com/ethereal\_wool/twitter-user-tweet-replies.md
- **Developed by:** [Jackie Chen](https://apify.com/ethereal_wool) (community)
- **Categories:** Social media, Marketing
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet
## Pricing
$3.00 / 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
## Twitter (X) User Replies Timeline Scraper
Scrape a **Twitter / X** user's **tweets-and-replies timeline** by handle. For each
entry you get the tweet text, the real author, the reply target, the conversation ID,
engagement counts (likes, retweets, replies, quotes, bookmarks, views), language,
media, and entities (mentions / hashtags / URLs). The Actor paginates the timeline by
cursor until your `maxItems` limit is reached.
> **Unofficial.** This Actor is not affiliated with, authorized, or endorsed by
> Twitter / X Corp. It is an independent tool that retrieves publicly available data
> via a third-party API. Use it in compliance with X's terms and all applicable laws;
> you are responsible for how you use the retrieved data.
### What it does
- **User timeline (tweets + replies)** โ give one or more handles (without the `@`).
The Actor returns the conversational timeline, which interleaves the user's own
tweets and their replies. Replies carry a non-null `inReplyToStatusId`.
- **Replies only (optional)** โ flip `repliesOnly` to keep only entries that are
replies.
- **Cursor pagination** โ keeps paging via `next_cursor` until `maxItems` is hit.
> Tip: pick accounts that actually reply. Handles that rarely reply (e.g. brand
> accounts) can return an empty timeline.
### Input
| Field | Type | Default | Description |
|---|---|---|---|
| `screenNames` | string[] | `["elonmusk"]` | X handles (without `@`) to scrape. Each is paginated independently. |
| `maxItems` | integer | `28` | Max total timeline entries across all handles. |
| `repliesOnly` | boolean | `false` | Keep only entries that are replies. |
#### Example input
```json
{
"screenNames": ["elonmusk", "paulg"],
"maxItems": 100,
"repliesOnly": false
}
````
### Output
One dataset item per tweet / reply:
```json
{
"tweetId": "2062561362719498706",
"url": "https://x.com/elonmusk/status/2062561362719498706",
"text": "Well said https://t.co/ks2LNlMy4C",
"lang": "en",
"createdAt": "Thu Jun 04 15:45:24 +0000 2026",
"source": "Twitter for iPhone",
"isReply": false,
"inReplyToStatusId": null,
"conversationId": "2062561362719498706",
"favorites": 2462,
"retweets": 406,
"replies": 430,
"quotes": 17,
"bookmarks": 360,
"views": 225091,
"author": {
"restId": "44196397",
"name": "Elon Musk",
"screenName": "elonmusk",
"followersCount": 240143467,
"blueVerified": true
},
"authorScreenName": "elonmusk",
"media": { "video": [], "photo": [] },
"entities": { "user_mentions": [] },
"scrapedFrom": "user:elonmusk"
}
```
### Notes
- The `author` of a reply entry is the actual author of that tweet, which may differ
from the queried handle when the entry sits inside a conversation the user joined.
- Data is sourced live; X occasionally rate-limits, so the Actor retries transient
blocks with exponential backoff.
- Tweet IDs are de-duplicated within a run.
### Quick start
1. Open the Actor and press **Run** โ the default input works out of the box.
2. Adjust the input fields below to your target (keywords, IDs, or URLs) and set `maxItems` to cap spend.
3. Grab results from the **Dataset** tab as JSON / CSV / Excel, or pull them via the [Apify API](https://docs.apify.com/api/v2) and MCP from your own code.
No proxies to configure, no cookies to paste, no login โ the Actor handles everything server-side.
### Why teams switch to this Twitter (X) user replies scraper
Seeing how an account engages โ not just what it broadcasts โ needs its replies,
which browser scrapers handle worst. This Actor reads a direct HTTP API and
returns an account's replies with full context and engagement as clean JSON at
**$3 per 1,000 replies**.
### What people build with it
- **Engagement-style analysis** โ how does a brand or exec actually talk to its
audience? Their replies show it.
- **Influencer vetting** โ gauge authenticity and responsiveness before a deal,
beyond follower counts.
- **Support auditing** โ review how a company handles complaints in public.
- **Conversation research** โ study the back-and-forth that keeps high-profile
threads alive.
- **Dialogue datasets** โ reply chains from a single voice for fine-tuning.
### Tips for better results
- Usernames work with or without `@`.
- Pair with [Twitter User Tweets Scraper](https://apify.com/ethereal_wool/twitter-user-tweets)
for original posts, and
[Twitter User Profile Scraper](https://apify.com/ethereal_wool/twitter-user-profile)
for bio and follower context.
- Engagement fields are numeric for easy ranking.
***
### Why this Actor
- **Direct API, no headless browser** โ fast, stable runs with nothing to babysit.
- **No login, no cookies** โ we never touch your accounts, so there's no ban risk.
- **Fresh, real-time data** โ every run reads the source live, not a stale cache.
- **Pay per result** โ you're billed only for the rows actually delivered.
- **Structured JSON** โ export to CSV, Excel, or JSON, or pull straight from the API / MCP.
### Use cases
- Mine audience sentiment and feature requests from real comment threads.
- Surface the most-liked replies and frequent questions under any post.
- Build moderation, UGC, or social-listening datasets at scale.
- Spot superfans and detractors by author and engagement.
### FAQ
**Do I need an account, cookies, or to log in anywhere?**
No. The Actor talks to a fast, direct HTTP API server-side โ you just provide inputs and run it.
**How am I billed?**
Pay-per-result: a fixed price per row returned, with no separate platform/compute charge. Caps like `maxItems` keep spend predictable.
**Can I run it on a schedule or call it from my app?**
Yes โ use Apify Schedules, the REST API, the JavaScript / Python clients, or the MCP server. See the **API** tab.
**Is this affiliated with Twitter (X)?**
No. It's an independent tool that collects publicly available data. Use it in line with the platform's terms and applicable law.
### More Twitter (X) scrapers by us
- [**Twitter (X) Search**](https://apify.com/ethereal_wool/twitter-tweet-search) โ Tweet search ยท Top & Latest ยท 30+ fields
- [**Twitter (X) User Tweets**](https://apify.com/ethereal_wool/twitter-user-tweets) โ User timeline ยท stats ยท media
- [**Twitter (X) Profile**](https://apify.com/ethereal_wool/twitter-user-profile) โ Profile ยท bio ยท follower counts
- [**Twitter (X) Tweet Replies**](https://apify.com/ethereal_wool/twitter-tweet-replies) โ Replies & comments on a tweet
Browse the full fleet โ **https://apify.com/ethereal\_wool**
# Actor input Schema
## `screenNames` (type: `array`):
Twitter / X handles (without the @) whose tweets-and-replies timeline you want to scrape, e.g. elonmusk. Each handle is paginated independently. Pick accounts that actually reply โ handles that rarely reply may return an empty timeline.
## `maxItems` (type: `integer`):
Maximum total number of timeline entries (tweets + replies) to scrape across all handles.
## `repliesOnly` (type: `boolean`):
Only keep entries that are replies (have a non-null in\_reply\_to\_status\_id). Off by default, which keeps the full tweets-and-replies timeline.
## `proxyConfiguration` (type: `object`):
Optional. Route the upstream API calls through an Apify Proxy to vary the source IP. Usually not needed.
## Actor input object example
```json
{
"screenNames": [
"elonmusk"
],
"maxItems": 28,
"repliesOnly": false,
"proxyConfiguration": {
"useApifyProxy": false
}
}
```
# 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 = {
"screenNames": [
"elonmusk"
]
};
// Run the Actor and wait for it to finish
const run = await client.actor("ethereal_wool/twitter-user-tweet-replies").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`๐พ 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);
});
// ๐ Want to learn more ๐? Go to โ 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 = { "screenNames": ["elonmusk"] }
# Run the Actor and wait for it to finish
run = client.actor("ethereal_wool/twitter-user-tweet-replies").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("๐พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# ๐ Want to learn more ๐? Go to โ https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"screenNames": [
"elonmusk"
]
}' |
apify call ethereal_wool/twitter-user-tweet-replies --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=ethereal_wool/twitter-user-tweet-replies",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "๐ฆ Twitter / X User Tweet Replies Scraper",
"description": "Extract Twitter / X user tweet replies data โ text, author, views, and more. Scrape by keyword, URL or ID. Export to JSON, CSV & Excel, use the API, schedule runs and integrate. No code required.",
"version": "0.1",
"x-build-id": "Tz2HMxmlajsVE4jyA"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/ethereal_wool~twitter-user-tweet-replies/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-ethereal_wool-twitter-user-tweet-replies",
"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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/ethereal_wool~twitter-user-tweet-replies/runs": {
"post": {
"operationId": "runs-sync-ethereal_wool-twitter-user-tweet-replies",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/ethereal_wool~twitter-user-tweet-replies/run-sync": {
"post": {
"operationId": "run-sync-ethereal_wool-twitter-user-tweet-replies",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"screenNames": {
"title": "Screen names (handles)",
"type": "array",
"description": "Twitter / X handles (without the @) whose tweets-and-replies timeline you want to scrape, e.g. elonmusk. Each handle is paginated independently. Pick accounts that actually reply โ handles that rarely reply may return an empty timeline.",
"default": [
"elonmusk"
],
"items": {
"type": "string"
}
},
"maxItems": {
"title": "Max items",
"minimum": 1,
"maximum": 100000,
"type": "integer",
"description": "Maximum total number of timeline entries (tweets + replies) to scrape across all handles.",
"default": 28
},
"repliesOnly": {
"title": "Replies only",
"type": "boolean",
"description": "Only keep entries that are replies (have a non-null in_reply_to_status_id). Off by default, which keeps the full tweets-and-replies timeline.",
"default": false
},
"proxyConfiguration": {
"title": "Proxy configuration",
"type": "object",
"description": "Optional. Route the upstream API calls through an Apify Proxy to vary the source IP. Usually not needed.",
"default": {
"useApifyProxy": false
}
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```