# X (Twitter) User Mentions Scraper - Brand Monitoring (`seemuapps/x-user-mentions-scraper`) Actor

Extract every tweet mentioning an X (Twitter) username - full text, author, and engagement metrics for brand monitoring and social listening.

- **URL**: https://apify.com/seemuapps/x-user-mentions-scraper.md
- **Developed by:** [Andrew](https://apify.com/seemuapps) (community)
- **Categories:** AI, Lead generation, Social media
- **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $3.00 / 1,000 mention 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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 (Twitter) User Mentions Scraper - Brand Monitoring

Extract every tweet mentioning an X (Twitter) username - full text, author, and engagement metrics for brand monitoring and social listening.

### What you get

- Mention text, tweet ID, URL, and creation timestamp
- Engagement metrics - likes, retweets, replies, quotes, views, bookmarks
- Author details - username, display name, follower count, verification
- Reply context - whether the mention is a reply, conversation ID, and who it replied to
- Cursor-based pagination - capture huge mention volumes across multiple runs

### Free tier vs. paid

Free runs are capped to help cover data costs:

- **25 mentions per run**
- **3 runs per day** (UTC)
- **30-minute wait** between runs

Upgrade to any paid plan on the actor's pricing page to lift all three limits - your `Max Mentions` setting is honored in full and runs are unrestricted.

### Use cases

- Brand monitoring - see every tweet that name-drops your brand or product handle
- Social listening - track sentiment and volume around a person or company in real time
- Customer service triage - catch complaints and support requests as they're posted
- Competitive intel - watch how people talk about a competitor's account
- PR and crisis monitoring - spot a spike in mentions the moment it happens

### How to use

1. Enter a **Username or profile URL** (with or without @, or a full `https://x.com/<username>` link)
2. Optionally set **Since** and **Until** (ISO 8601 datetimes) to bound the time window
3. Set **Max Mentions** (default 100; 0 for unlimited)
4. Run the actor - one mentioning tweet per row in the **Dataset** tab
5. To fetch more mentions, open the **Key-value store** tab → copy the `NEXT_PAGE_ID` value → paste it into **Page ID** on your next run

### Output format

One mentioning tweet per dataset row - perfect for direct CSV, Excel, or Google Sheets export:

```json
{
  "mentionedUsername": "elonmusk",
  "tweetId": "1847001234567890",
  "url": "https://x.com/someuser/status/1847001234567890",
  "text": "Great point @elonmusk made about…",
  "createdAt": "Wed Jan 01 12:05:00 +0000 2026",
  "lang": "en",
  "likeCount": 128,
  "replyCount": 7,
  "retweetCount": 14,
  "quoteCount": 2,
  "viewCount": 9800,
  "bookmarkCount": 3,
  "isReply": false,
  "conversationId": "1847001234567890",
  "authorUsername": "someuser",
  "authorName": "Some User",
  "authorIsBlueVerified": true,
  "authorFollowersCount": 24500
}
```

### Pagination

If the account has more mentions than **Max Mentions** allows, the actor saves a resume cursor to the default **Key-value store** under the key `NEXT_PAGE_ID`.

1. Open the **Key-value store** tab on the run page
2. Copy the value of `NEXT_PAGE_ID`
3. Start a new run and paste it into **Page ID**

When `NEXT_PAGE_ID` is `null`, all mentions in the requested time window have been fetched.

### Input options

| Field | Type | Description |
|-------|------|-------------|
| Username or profile URL | string | X username, with or without @, or a full profile URL (required) |
| Max Mentions | integer | Cap per run - default 100, 0 for unlimited |
| Since (optional) | string | ISO 8601 datetime - only mentions after this time |
| Until (optional) | string | ISO 8601 datetime - only mentions before this time |
| Page ID | string | `NEXT_PAGE_ID` from the previous run's Key-value store, to resume pagination |

### Related X (Twitter) actors

Part of a complete X (Twitter) toolkit - explore the rest of the suite:

- [X (Twitter) Profile Scraper](https://apify.com/seemuapps/x-profile-scraper) - Public profile data - bio, counts, links
- [X (Twitter) User Tweets Scraper](https://apify.com/seemuapps/x-user-tweets-by-username) - Full timeline of any account by username
- [X (Twitter) Tweet Scraper](https://apify.com/seemuapps/x-tweet-scraper) - Search and export tweets by query, hashtag, user
- [X (Twitter) Tweet Replies Scraper](https://apify.com/seemuapps/x-tweet-replies-scraper) - Every reply under a post for sentiment analysis
- [X (Twitter) Quote Tweets Scraper](https://apify.com/seemuapps/x-quote-tweets-scraper) - Every quote tweet of a post, with engagement
- [X (Twitter) Followers & Following Scraper](https://apify.com/seemuapps/x-followers-following-scraper) - Full followers and following lists, no login
- [X (Twitter) List Members Scraper](https://apify.com/seemuapps/x-list-members-scraper) - Members or followers of any public list
- [X (Twitter) Active Hours Analyzer](https://apify.com/seemuapps/x-active-hours-analyzer) - Posting heatmap, peak hours, and time zone
- [X (Twitter) Retweeters Scraper](https://apify.com/seemuapps/x-tweet-retweeters-scraper) - Find every account that retweeted a post
- [X (Twitter) User Search Scraper](https://apify.com/seemuapps/x-user-search-scraper) - Find accounts by bio or keyword for lead gen
- [X (Twitter) Trends Scraper](https://apify.com/seemuapps/x-trends-scraper) - Trending topics and hashtags by location
- [X (Twitter) Article to Markdown Scraper](https://apify.com/seemuapps/x-article-to-markdown-scraper) - Long form Articles as clean Markdown
- [X (Twitter) MCP Server](https://apify.com/seemuapps/x-mcp) - All X tools for AI agents via MCP

# Actor input Schema

## `username` (type: `string`):

X (Twitter) username, with or without @, or a full profile URL (https://x.com/<username>), whose mentions you want to extract.

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

Maximum mentioning tweets to return. Set 0 for unlimited.

## `sinceTime` (type: `string`):

ISO 8601 datetime, e.g. 2026-01-01T00:00:00Z - converted to a unix timestamp automatically. Only mentions after this time are returned.

## `untilTime` (type: `string`):

ISO 8601 datetime, e.g. 2026-01-01T00:00:00Z - converted to a unix timestamp automatically. Only mentions before this time are returned.

## `pageId` (type: `string`):

Optional. Cursor from a previous run to resume.

## Actor input object example

```json
{
  "username": "elonmusk",
  "maxItems": 100
}
```

# Actor output Schema

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

One row per mentioning tweet: mentionedUsername, tweetId, url, text, createdAt, lang, engagement counts, conversation context, author info.

## `nextPageId` (type: `string`):

NEXT\_PAGE\_ID record in the default key-value store. Paste into Page ID on the next run to resume; null when all mentions are fetched.

# 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 = {
    "username": "elonmusk"
};

// Run the Actor and wait for it to finish
const run = await client.actor("seemuapps/x-user-mentions-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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "username": "elonmusk" }

# Run the Actor and wait for it to finish
run = client.actor("seemuapps/x-user-mentions-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 '{
  "username": "elonmusk"
}' |
apify call seemuapps/x-user-mentions-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=seemuapps/x-user-mentions-scraper",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/TAJqzhKU5ZrRNFTjE/builds/s1CZgU4nUbHp9j4DB/openapi.json