# Twitter/X Comments Scraper · No Cookies (`data-slayer/twitter-comments`) Actor

Extract replies and comments from any tweet without login. Get comment text, author profiles, likes, retweets, bookmarks, views, timestamps, and conversation threads. Sentiment analysis ready. No cookies, no authentication, no API key. JSON/CSV/Excel export.

- **URL**: https://apify.com/data-slayer/twitter-comments.md
- **Developed by:** [Data Slayer](https://apify.com/data-slayer) (community)
- **Categories:** Social media, Developer tools, Automation
- **Features:** Limited permissions
- **Stats:** 381 total users, 35 monthly users, 99.4% runs succeeded, 3 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.50 / 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.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Twitter Comments Scraper - No Login Needed

Extract Twitter post comments at scale without authentication or cookies. This scraper delivers comprehensive comment data, engagement metrics, and author profiles in seconds—perfect for sentiment analysis, competitive research, and content strategy optimization.

### 📺 Video Tutorial: How it Works

https://www.youtube.com/watch?v=X7Jn9epGPwY

***

### Inputs

| Field | Type | Description |
|-------|------|-------------|
| tweetId | String | One X/Twitter post URL or numeric ID (e.g., `2024799518181700017`, or `https://x.com/user/status/2024799518181700017`). Copy the numeric string after `/status/`. |
| tweetIds | String\[] | Optional batch of up to 100 post URLs or numeric IDs. Duplicate IDs are processed once, so the whole run is de-duplicated. |
| maxItems | Integer | Maximum unique comment rows across the whole run. Default `1000`, max `50000`. |
| maxPages | Integer | Safety limit on pages fetched per post. The run stops at `maxItems`, cursor exhaustion, a repeated cursor, or this page limit — whichever comes first. Default `1`. |

### Outputs

**Formats**: JSON, CSV, Excel

**Fields**: Each comment includes:

- `id` / `tweet_id` - Unique comment identifier
- `text` - Full comment text content
- `display_text` - Cleaned text without mentions/URLs
- `likes`, `retweets`, `bookmarks`, `quotes`, `replies` - Engagement metrics
- `views` - Impression count
- `created_at` - Timestamp
- `lang` - Detected language
- `conversation_id` - Thread identifier
- `reply_to_tweet_id` - The comment this one replies to
- `is_retweet` - Whether the row is a retweet
- `author.name`, `author.screen_name`, `author.description` - Profile details
- `author.blue_verified`, `author.sub_count` - Verification and follower count

### Sample Output

```json
{
  "id": "1738107442452381976",
  "text": "@elonmusk Gratitude is a musk thank you Elon [https://t.co/8u6rbvMYKp](https://t.co/8u6rbvMYKp)",
  "display_text": "Gratitude is a musk thank you Elon",
  "likes": 822,
  "retweets": 69,
  "bookmarks": 4,
  "quotes": 2,
  "replies": 152,
  "views": "56888",
  "created_at": "Fri Dec 22 08:01:21 +0000 2023",
  "lang": "en",
  "conversation_id": "1738106896777699464",
  "author": {
    "rest_id": "2800216425",
    "name": "THE CHELSEA FORUM",
    "screen_name": "TheChelseaForum",
    "description": "The Chelsea Forum ◇|◇ A community where all Chelsea fans call their home! 💙",
    "blue_verified": true,
    "sub_count": 259816
  }
}
```

***

### How to Use

**Step 1**: Enter the target Tweet ID in the `tweetId` field. Copy this from the Twitter post URL (the numeric string after `/status/`).

**Step 2**: Click "Start" to begin extraction.

**Step 3**: Download your results in JSON, CSV, or Excel format once the scraper completes.

### Key Features

🔒 **Cookieless / No Login Required** - Access public Twitter comment data without authentication, cookies, or API keys. No account suspension risks or rate limit headaches.

📈 **Scalable Architecture** - Extract thousands of comments from viral threads with enterprise-grade infrastructure that handles high-volume data collection reliably.

🧵 **Single post or a whole batch** - Pass one post in `tweetId`, or up to 100 at once in `tweetIds`; duplicates are collapsed so you never pay twice for the same comment.

✅ **Rich Comment Data** - Capture complete engagement metrics including likes, retweets, bookmarks, quotes, replies, views, timestamps, and conversation threads with full author profiles.

### Use Cases

**Social Media Managers**: Monitor brand mentions and audience reactions across Twitter threads. Track sentiment shifts in real-time to adjust campaign messaging and identify emerging trends before competitors.

**Market Research Analysts**: Collect qualitative feedback from product launches, industry discussions, and competitor announcements. Analyze comment patterns to uncover customer pain points and feature requests.

**Sales Intelligence Teams**: Identify high-intent prospects engaging with industry thought leaders. Build targeted outreach lists based on comment activity, follower counts, and verification status.

### FAQ

**How much does it cost to run this Actor?**
Pricing is listed in the Pricing section above, charged per row returned. You only pay for the rows
the Actor actually produces, so a small test run costs cents.

**Do I need cookies or a login?**
No. This Actor reads public data only - no cookies, no account, no browser automation.

**Can I scrape several posts in one run?**
Yes. Put the first post in `tweetId` and up to 100 more in `tweetIds`; the Actor de-duplicates
repeated IDs and returns every unique comment once.

**Can I schedule it?**
Yes. Run it from the Apify API, on a schedule, or via a webhook, and export results as JSON, CSV or Excel.

**What if I need a slightly different output?**
Open an issue on the Actor's Issues tab describing the shape you need.

### Related actors

More Twitter actors from the same portfolio. Apify only auto-recommends actors in the same category, so here are the rest of ours:

| Actor |
|---|
| [Twitter/X Community Members Scraper · No Cookies](https://apify.com/data-slayer/twitter-community-members) |
| [Twitter/X Engaged Audience Exporter · Replies + Retweeters](https://apify.com/data-slayer/twitter-engaged-audience-exporter) |
| [Twitter/X Followers Scraper · No Cookies](https://apify.com/data-slayer/twitter-followers) |
| [Twitter/X Following Scraper · Public Profiles](https://apify.com/data-slayer/twitter-followings) |
| [Twitter/X List Members Scraper · No Cookies](https://apify.com/data-slayer/twitter-list-members) |
| [Twitter/X Profile Scraper · Bulk Handles, URLs & IDs](https://apify.com/data-slayer/twitter-user) |
| [Twitter/X Search Scraper · Small Jobs & Advanced Queries](https://apify.com/data-slayer/twitter-search) |
| [Twitter/X Trends Scraper · No Cookies](https://apify.com/data-slayer/twitter-trends-by-location) |
| [Twitter/X Tweet Details Scraper · URL or ID](https://apify.com/data-slayer/twitter-tweet-details) |
| [Twitter/X Tweets Scraper · No Cookies](https://apify.com/data-slayer/twitter-user-tweets) |

# Actor input Schema

## `tweetId` (type: `string`):

One x.com/twitter.com status URL or numeric tweet ID. The existing tweetId key remains supported.

## `tweetIds` (type: `array`):

Optional batch of up to 100 x.com/twitter.com status URLs or numeric tweet IDs. Duplicate IDs are processed once.

## `maxItems` (type: `integer`):

Maximum unique comment rows across the whole run.

## `maxPages` (type: `integer`):

Legacy-compatible safety limit. The run stops at maxItems, cursor exhaustion, a repeated cursor, or this page limit, whichever comes first.

## Actor input object example

```json
{
  "tweetId": "2024799518181700017",
  "maxItems": 1000,
  "maxPages": 1
}
```

# Actor output Schema

## `results` (type: `string`):

One default-dataset item per unique accepted comment, written through unchanged.

## `runSummary` (type: `string`):

Target, page, duplicate, empty, partial, cancellation, and billing counts. This record is not a default-dataset item.

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("data-slayer/twitter-comments").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("data-slayer/twitter-comments").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call data-slayer/twitter-comments --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,data-slayer/twitter-comments"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/BhkiOZwFclARKOiBR/builds/uJCO5ggeszdLPC37T/openapi.json