# Emerging Trend Intelligence (`northpeak_data/emerging-trend-intelligence`) Actor

Spot signals before they become obvious. Turn social data into ranked emerging trends using viral momentum, persistence, corroboration, counter-signals, confidence, and action-ready decisions.

- **URL**: https://apify.com/northpeak\_data/emerging-trend-intelligence.md
- **Developed by:** [Northpeak Data](https://apify.com/northpeak_data) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $9.90 / 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/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

## Emerging Trend Intelligence

**Spot emerging trends before they become obvious — turn noisy social data into ranked, evidence-backed opportunities.**

Emerging Trend Intelligence transforms TikTok and social-content datasets into a decision layer built for brands, agencies, ecommerce teams, researchers, investors, creators, and trend-driven businesses.

Instead of returning another feed of posts, hashtags, views, and likes, it answers the questions behind the data: **What is accelerating? Is the signal persistent or just a viral spike? Is it corroborated across multiple records? What evidence argues against the trend? And which signals deserve investigation now?**

### What you get

- **Trend ranking** based on reach, engagement, and signal strength
- **Viral anomaly detection** to identify unusual acceleration
- **Persistence analysis** to distinguish momentum from one-off spikes
- **Corroboration signals** across multiple records
- **Counter-signals** so weak or contradictory evidence is not ignored
- **Evidence quality** for judging how much the underlying data can support
- **Confidence score** for every trend analysis
- **Action-ready decision**: investigate now, watch closely, watch, or collect more evidence
- **Top emerging topics and hashtags** extracted from supplied social data

### Built above the scraper layer

This Actor does **not** try to replace TikTok or social-data scrapers. Feed it records directly or provide an Apify dataset ID from an upstream scraper. Emerging Trend Intelligence analyzes that raw data and turns it into decision intelligence.

The workflow is automation-ready: **collect → detect → rank → investigate**.

### Typical use cases

- Discover emerging consumer trends
- Identify early product opportunities
- Monitor category momentum
- Prioritize trends for ecommerce research
- Detect viral signals before they become obvious
- Support brand, content, and competitive-intelligence decisions
- Run recurring social-market monitoring

### How it works

The Actor normalizes hashtags and text/caption signals, aggregates mentions, views, likes, comments, and shares, and evaluates each candidate using engagement, per-post reach, anomaly, persistence, corroboration, and evidence quality. It also returns counter-signals so a large number alone does not automatically become a strong recommendation.

### Input

Use either:

- social-content records directly, or
- an Apify dataset ID from an upstream social scraper.

Use `topN` to control how many ranked trend signals are returned.

### Output

Structured JSON is ready for Apify datasets, APIs, agents, alerts, spreadsheets, dashboards, and downstream automations. Key fields include signal score, evidence quality, counter-signals, confidence, and decision.

### Important interpretation note

Trend detection is probabilistic. The Actor identifies signals in the supplied dataset; it does not claim that a trend will continue. Confidence, evidence quality, and counter-signals are included to keep the analysis transparent.

# Actor input Schema

## `records` (type: `array`):

Paste TikTok/social records. Common caption/text, views, likes, comments, shares, hashtags and timestamp fields are normalized.

## `datasetId` (type: `string`):

Analyze an existing Apify dataset instead of pasted records.

## `topN` (type: `integer`):

Maximum number of ranked emerging trends to return.

## Actor input object example

```json
{
  "records": [
    {
      "text": "protein coffee morning routine",
      "views": 900000,
      "likes": 95000,
      "comments": 4200,
      "shares": 18000,
      "hashtags": [
        "proteincoffee",
        "morningroutine"
      ]
    },
    {
      "text": "protein coffee recipe",
      "views": 600000,
      "likes": 72000,
      "comments": 2500,
      "shares": 12000,
      "hashtags": [
        "proteincoffee"
      ]
    },
    {
      "text": "old trend",
      "views": 10000,
      "likes": 200,
      "comments": 20,
      "shares": 10,
      "hashtags": [
        "oldtrend"
      ]
    }
  ],
  "topN": 20
}
```

# 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("northpeak_data/emerging-trend-intelligence").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("northpeak_data/emerging-trend-intelligence").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 northpeak_data/emerging-trend-intelligence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,northpeak_data/emerging-trend-intelligence"
        }
    }
}
```

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/lTZYLbHpmFloYqcMa/builds/8bR3eLDYGKdcC6MMq/openapi.json