# Crypto Influencer Tracker - Twitter/Reddit Sentiment & Signals (`gochujang/crypto-influencer-tracker`) Actor

Track crypto influencer activity and sentiment from public sources. Monitors Nitter RSS (Twitter mirror) and Reddit user posts. Extracts token mentions, sentiment scores, price predictions, and per-influencer summaries. No API key required.

- **URL**: https://apify.com/gochujang/crypto-influencer-tracker.md
- **Developed by:** [Hojun Lee](https://apify.com/gochujang) (community)
- **Categories:** Business
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 1,000 item trackeds

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/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

### What does Crypto Influencer Tracker do?

**Crypto influencer tracker** that monitors posts from top traders, analysts, and thought leaders on **Twitter (via Nitter RSS)** and **Reddit** — no API key, no proxy required. For each influencer it extracts **token mentions**, computes a keyword-based **sentiment score** (−100 to +100), flags **price predictions**, and produces a per-influencer summary with 7-day average sentiment and trend direction.

This **social crypto signals** tool turns unstructured KOL activity into structured, queryable data you can feed directly into a trading system, alert pipeline, or research dashboard. It is a practical **Twitter crypto KOL monitor** built for teams that cannot afford the $100+/month Twitter API.

### Why use Crypto Influencer Tracker?

**Crypto influencer tracker** data is used across trading, research, and product teams:

1. **KOL-driven trade signals** — When a high-follow influencer flips from bearish to bullish on a token, price often follows within hours. The actor's `sentiment_trend` field (`improving` / `stable` / `deteriorating`) captures this shift automatically.
2. **Narrative detection** — `mentioned_tokens` aggregated across influencers reveals which tokens the market is talking about before volume confirms the move.
3. **Price prediction alerting** — `is_price_prediction: true` posts (containing keywords like "target", "will reach", "TP") are flagged so you can fast-track review of actionable calls.
4. **Sentiment baseline** — Track `avg_sentiment_7d` per influencer to distinguish chronic bulls from genuine momentum shifts.
5. **Research & compliance** — Build a timestamped archive of **Twitter crypto KOL** activity for backtesting or regulatory documentation without paying for the Twitter API.

### How to use Crypto Influencer Tracker

1. Click **Try for Free** on the Apify Store page.
2. Leave `handles` at the 8 curated defaults or add your own (e.g. `["VitalikButerin","CryptoGodJohn"]`).
3. Set `lookbackHours` (default `48`) and `maxPostsPerHandle` (default `10`) to control data volume.
4. Click **Start** — results are ready in 30–90 seconds depending on handle count.
5. Use the [Apify API](https://docs.apify.com/api/v2) or a scheduled run to power continuous **social crypto signal** monitoring.

### Input

| Parameter | Default | Description |
|---|---|---|
| `handles` | 8 curated influencers | Twitter/Reddit usernames to track |
| `platforms` | `["nitter","reddit"]` | Data sources: `nitter` (Twitter mirror), `reddit` |
| `maxPostsPerHandle` | `10` | Max posts per influencer per platform per run |
| `nitterInstances` | 3 mirrors | Nitter instances to try in order (fallback chain) |
| `lookbackHours` | `48` | Only include posts from the last N hours (0 = no filter) |

**Default influencers tracked**: `saylor`, `CryptoCapo_`, `PeterLBrandt`, `WClementeIII`, `IncomeSharks`, `raoulGMI`, `stacy_muur`, `inversebrah`

### Output

Two record types per run — per-post and per-influencer summary:

```json
[
  {
    "username": "saylor",
    "platform": "twitter",
    "post_date": "2026-09-01T14:22:00Z",
    "post_text": "Bitcoin is the apex property of the human race. $BTC target $500K by 2030.",
    "mentioned_tokens": ["BTC"],
    "sentiment_score": 92,
    "sentiment_label": "very_bullish",
    "is_price_prediction": true,
    "engagement_proxy": 14200,
    "hours_since_post": 3.5
  },
  {
    "influencer_summary": true,
    "username": "saylor",
    "total_posts_fetched": 8,
    "posts_24h": 3,
    "avg_sentiment_7d": 87.4,
    "most_mentioned_token": "BTC",
    "sentiment_trend": "stable"
  }
]
```

### Data fields

#### Per-post record

| Field | Type | Description |
|---|---|---|
| `username` | string | **Influencer handle** |
| `platform` | string | `twitter` or `reddit` |
| `post_date` | ISO8601 | UTC timestamp |
| `post_text` | string | Post text (truncated to 280 chars) |
| `mentioned_tokens` | array | Token tickers found (`BTC`, `ETH`, `SOL`, …) |
| `sentiment_score` | integer | −100 (very bearish) to +100 (very bullish) |
| `sentiment_label` | string | `very_bullish` / `bullish` / `neutral` / `bearish` / `very_bearish` |
| `is_price_prediction` | boolean | `true` if post contains price prediction language |
| `engagement_proxy` | integer | Likes + retweets (from RSS when available) |
| `hours_since_post` | float | Hours elapsed since publication |

#### Per-influencer summary record

| Field | Type | Description |
|---|---|---|
| `influencer_summary` | boolean | `true` — marks summary rows |
| `username` | string | Handle |
| `total_posts_fetched` | integer | Posts collected this run |
| `posts_24h` | integer | Posts in last 24 hours |
| `avg_sentiment_7d` | float | **7-day average sentiment score** |
| `most_mentioned_token` | string | Most frequently mentioned token |
| `sentiment_trend` | string | `improving` / `stable` / `deteriorating` |

### Cost estimation

**Pay-Per-Event pricing** at **$0.005 per influencer tracked**.

| Influencers | Cost/run | Daily (1×/day) | Hourly monitoring |
|---|---|---|---|
| 8 (default) | $0.04 | $0.04 | $0.96 |
| 20 influencers | $0.10 | $0.10 | $2.40 |
| 50 influencers | $0.25 | $0.25 | $6.00 |

### FAQ

**Q: Why use Nitter instead of the official Twitter API for Twitter crypto KOL monitoring?**
The official Twitter/X API costs $100–$5,000/month for research-level access. **Nitter** is a public RSS mirror that requires no authentication. This actor tries multiple Nitter instances automatically and falls back to Reddit if all mirrors are unavailable.

**Q: How accurate is the sentiment scoring?**
The **sentiment score** is keyword-based (not ML). It performs well on English-language posts with explicit bullish/bearish vocabulary (e.g. "moon", "dump", "ATH", "rekt"). Sarcasm and nuanced phrasing are not reliably detected — treat scores as a signal filter, not ground truth.

**Q: Can I track non-English influencers?**
The token extraction and sentiment keywords are English-optimized. Non-English posts are collected and returned but sentiment scores will be unreliable. Multi-language support is on the roadmap.

***

**Disclaimer**: Influencer posts are reproduced from public RSS feeds for informational purposes only. Sentiment scores are algorithmic estimates and do not constitute investment advice. Social signals should be one input among many in any trading or research process.

***

### Data sources

- **Nitter** — public Twitter/X RSS mirror, no authentication required; actor uses a fallback chain of 3 instances
- **Reddit** — public `reddit.com/user/{handle}/submitted.json` endpoint

### Related actors

- [Crypto Exchange Listings Tracker](https://apify.com/gochujang/crypto-exchange-listings) — New listings that influencers often discuss first
- [Long/Short OI Aggregator](https://apify.com/gochujang/long-short-oi-aggregator) — Confirm sentiment with open interest positioning
- [Hyperliquid Whale Tracker](https://apify.com/gochujang/hyperliquid-whale-tracker) — Follow smart money behind the KOL noise

***

**Keywords:** crypto influencer tracker, KOL sentiment analysis, crypto Twitter sentiment, influencer token mentions, crypto social signals, Nitter RSS scraper, Reddit crypto tracker, influencer price prediction, crypto KOL monitor, sentiment score crypto, social media crypto signals, influencer portfolio tracker

# Actor input Schema

## `handles` (type: `array`):

List of Twitter/Reddit usernames to track. For Twitter/Nitter use @-less handles (e.g. 'saylor'). For Reddit, same convention.

## `platforms` (type: `array`):

Which platforms to fetch posts from: 'nitter' (Twitter mirror RSS) and/or 'reddit'

## `maxPostsPerHandle` (type: `integer`):

Maximum number of recent posts to fetch per influencer per platform

## `nitterInstances` (type: `array`):

Nitter mirror URLs to try in order (fallback chain). Leave default to use built-in list.

## `lookbackHours` (type: `integer`):

Only include posts from the last N hours. 0 = no time filter.

## Actor input object example

```json
{
  "handles": [
    "saylor",
    "CryptoCapo_",
    "PeterLBrandt",
    "WClementeIII",
    "IncomeSharks",
    "raoulGMI",
    "stacy_muur",
    "inversebrah"
  ],
  "platforms": [
    "nitter",
    "reddit"
  ],
  "maxPostsPerHandle": 10,
  "nitterInstances": [
    "https://nitter.net",
    "https://nitter.privacydev.net",
    "https://nitter.poast.org"
  ],
  "lookbackHours": 48
}
```

# Actor output Schema

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

No description

# 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("gochujang/crypto-influencer-tracker").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("gochujang/crypto-influencer-tracker").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 gochujang/crypto-influencer-tracker --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,gochujang/crypto-influencer-tracker"
        }
    }
}
```

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/FKnv8p39Czwy9LMLy/builds/NfT8N1t6vdOOo5vxX/openapi.json