# China Cross-Platform Trend & KOL Analyzer (`quanmatrix/china-cross-platform-trend-kol-intelligence`) Actor

Combine supplied Weibo, RedNote, Bilibili and other China-social records into topic momentum, creator signals and cross-platform convergence reports. Compare snapshots, rank trends, and export structured decisions for social research and automation. No live login or restricted content collection.

- **URL**: https://apify.com/quanmatrix/china-cross-platform-trend-kol-intelligence.md
- **Developed by:** [Rafael Barreto Haddad](https://apify.com/quanmatrix) (community)
- **Categories:** Social media, Automation, AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.95 / 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

## China Cross-Platform Trend & KOL Analyzer

Combine supplied Weibo, RedNote, Bilibili and other China-social records into topic momentum, creator signals and cross-platform convergence reports. Compare snapshots, rank trends, and export structured decisions for social research and automation. No live login or restricted content collection.

### Why use this Actor

Teams monitoring China social platforms need one comparable decision layer across incompatible schemas, rankings and engagement metrics instead of manually reconciling separate exports.

### Key features

- Buyer-native inputs aligned to **cross platform social intelligence**.
- Predictable structured output in the default Apify Dataset.
- Pay Per Event pricing tied to measurable result rows or reports.
- Optional MCP connector delivery for downstream automation and AI agents.

### Quick start

Start with this working example and replace the target values with your own:

```json
{
  "records": [
    {
      "platform": "Weibo",
      "contentId": "example-weibo",
      "title": "AI wearable launch",
      "topic": "AI wearable",
      "creator": "Brand Example",
      "engagement": 1400,
      "engagementDelta": 240,
      "rank": 3,
      "rankDelta": 2
    },
    {
      "platform": "RedNote",
      "contentId": "example-rednote",
      "title": "AI wearable hands-on",
      "topic": "AI wearable",
      "creator": "Creator Example",
      "engagement": 1000,
      "engagementDelta": 190,
      "rank": 5,
      "rankDelta": 2
    }
  ]
}
```

The Actor writes structured results to the default Apify Dataset and can be used from the Store, API, schedules, Tasks, automations and MCP-compatible AI workflows.

### Input

- `records` — Snapshot records: Rows from upstream public-platform collectors. Each row should include platform plus topic/title, with optional creator, engagement, engagementDelta, rank and rankDelta.
- `minPlatforms` — Minimum platforms: Return only signals observed on at least this many distinct platforms.
- `minMomentumScore` — Minimum momentum score: Return only fused signals at or above this 0-100 momentum threshold.

All integration and advanced analysis fields are optional. The default example is intentionally runnable without configuring MCP or a previous-run baseline.

### Output

The Dataset exposes predictable machine-readable output. Representative fields include `results`.

### Pricing

This Actor uses Pay Per Event. The current factory base price is **$0.002500 per result event**. The Apify Store remains the source of truth for the price and plan/tier details shown to the buyer.

### Use cases

- Run the buyer-ready workflows exposed as Apify Tasks without preparing a custom integration first.
- Use the Actor from API or schedules for recurring collection, comparison or monitoring.
- Feed the structured Dataset output into spreadsheets, databases, automations or AI agents.
- Compare repeat runs when the product supports snapshots or previous-run inputs.

### Automation and AI

Use the same Actor through Apify API, schedules, public Tasks and the Apify MCP server. Outputs are structured for downstream workflows and AI agents rather than requiring manual copy/paste.

### Limitations

- Public websites and upstream APIs can change markup, access rules, rate limits or field availability without notice.
- Fields that are not publicly available are returned as unavailable or omitted rather than fabricated.
- Analytical outputs depend on the quality and coverage of the supplied or collected source data.
- Treat marketplace, reputation, workforce, safety or commercial signals as decision support and validate material decisions against the underlying source evidence.

### Detailed documentation

### Detailed documentation

### Detailed documentation

Use this Actor to analyze china cross-platform trend and kol and return decision-ready structured signals. It is designed for repeatable human, API, Apify AI, and MCP-driven workflows.

China Cross-Platform Trend & KOL Intelligence is a decision layer for teams that already collect public data from Chinese social platforms and need one comparable signal surface across incompatible schemas. It accepts snapshot rows from sources such as Weibo, RedNote / Xiaohongshu, Bilibili, Kuaishou / Kwai, and WeChat-family collectors, normalizes the platform labels, groups records by topic, scores momentum, measures platform breadth, and emits deterministic actions for analysts and AI agents.

### What this Actor does

Instead of pretending that every Chinese platform exposes the same metrics, the Actor preserves the upstream evidence and derives a normalized intelligence layer. Each input row can carry a platform, content ID, title or topic, creator, engagement, engagement delta, rank, and rank delta. Missing optional fields are tolerated. The Actor computes a per-record momentum estimate, fuses rows sharing the same normalized topic, counts distinct platforms and creators, and produces one decision-ready signal.

The main output fields are `topic`, `platformCount`, `platforms`, `recordCount`, `creatorCount`, `combinedMomentumScore`, `agentAction`, and `topEvidence`. Actions are deterministic: `CROSS_PLATFORM_BREAKOUT` identifies strong topics confirmed across at least three platforms, `CONVERGING_TREND` identifies multi-platform momentum, `PLATFORM_SURGE` identifies a strong but narrower surge, and `WATCH` keeps weaker signals visible without overstating them.

### Why use this Actor

Chinese social monitoring is fragmented. A Weibo hot-search row, a RedNote note, a Bilibili video, and a Kuaishou creator signal do not naturally line up. Raw multi-platform exports therefore create a second problem: somebody still has to decide whether several records describe the same market movement and whether that movement is broad enough to matter.

This Actor focuses on that second problem. It is intentionally separate from transport-specific collectors. That means a collector can evolve from HTML to MTOP, browser, proxy, or another legitimate public-data surface without forcing downstream analysts to rewrite their fusion logic.

### Key features

- China-focused platform normalization across heterogeneous upstream schemas.
- Cross-platform topic convergence and platform-breadth measurement.
- Creator/KOL counts with retained top evidence for auditability.
- Engagement-delta and rank-delta momentum scoring.
- Deterministic agent actions for breakouts, convergence, platform surges, and watch signals.
- No platform credentials required because the Actor operates on supplied public-data snapshots.

### Input

`records` is required and accepts up to 5,000 upstream snapshot rows per run. `minPlatforms` filters out signals that do not appear across enough distinct platforms. `minMomentumScore` applies a 0-100 decision threshold after fusion. A safe example is prefilled in the Apify Console so the default run works without credentials or external setup.

### Output

Every dataset row represents one fused topic signal. `topEvidence` retains the strongest contributing records with platform, content ID, title, creator, engagement, engagement delta, rank, rank delta, and the normalized per-record momentum score. This makes every decision traceable instead of turning the score into unexplained numerical theater.

### Typical use cases

Use the Actor for China trend monitoring, KOL momentum analysis, launch tracking, campaign propagation, reputation escalation, consumer-category research, competitor narrative monitoring, and agentic market-intelligence pipelines. Twenty task blueprints cover distinct operational intents including daily convergence, breakout radar, product launches, automotive, beauty, gaming, travel, retail, crisis monitoring, and agent-ready priority feeds.

### Product advantage

The Actor is not another generic multi-network scraper. Its first advantage is functional: it provides a China-focused normalization contract for upstream Weibo, RedNote, Bilibili, Kuaishou, and WeChat-family snapshot rows. Its second advantage is analytical: it turns platform breadth plus engagement and rank movement into deterministic convergence actions. Current direct multi-platform competitors reviewed in the Apify Store emphasize extraction, unified rows, account metrics, rankings, or engagement, but do not document this China-specific convergence-to-action layer.

### Reliability and limitations

The Actor does not bypass logins, CAPTCHAs, anti-bot controls, private APIs, or platform terms. It only fuses data supplied by the caller. Therefore the freshness and completeness of the result depend on the upstream snapshots. Engagement values also differ by platform, so the momentum score is a practical normalization heuristic rather than a claim that likes, views, comments, and rank positions are economically identical.

### Pricing

The product uses one primary pay-per-result event: one fused decision-ready cross-platform signal. There is no start-fee design in the local factory configuration. Tiered pricing is applied by the factory where the Apify pricing surface supports it.

### Automation and AI agents

The stable schema is designed for downstream automation. Agents can filter on `agentAction`, `platformCount`, or `combinedMomentumScore`, inspect `topEvidence`, and route only high-confidence signals into research, alerts, reports, or campaign workflows. Because the decisions are deterministic and evidence-backed, the same input produces explainable output rather than an opaque model guess.

# Changelog

This Actor's version history is a separate document: https://apify.com/quanmatrix/china-cross-platform-trend-kol-intelligence/changelog.md

# Actor input Schema

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

Rows from upstream public-platform collectors. Each row should include platform plus topic/title, with optional creator, engagement, engagementDelta, rank and rankDelta.

## `minPlatforms` (type: `integer`):

Return only signals observed on at least this many distinct platforms.

## `minMomentumScore` (type: `number`):

Return only fused signals at or above this 0-100 momentum threshold.

## `previousAnalysis` (type: `object`):

Optional prior Gen2 output used to calculate decision-metric deltas and regression.

## `valuePerImpactUnitUsd` (type: `number`):

Optional user-supplied economic value per impact unit. Leave empty to avoid monetary estimation.

## `monthlyRuns` (type: `integer`):

Optional expected monthly run count used only with valuePerImpactUnitUsd for economic impact estimation.

## `mcpConnectors` (type: `array`):

Optional MCP connectors authorized in your Apify account. Use them to send or write this Actor result to tools such as Slack, Notion, GitHub, Sentry, Supabase, or another compatible MCP service.

## `mcpToolName` (type: `string`):

Optional exact MCP tool name. Leave blank to let the selected MCP action preset discover a compatible tool automatically.

## `mcpToolArguments` (type: `object`):

JSON object passed to the selected MCP tool. String values may use {{actor_title}}, {{result_summary}}, or {{result_json}} placeholders.

## `mcpFailOnError` (type: `boolean`):

When enabled, an MCP delivery error fails the Actor run. Disabled by default so data extraction and intelligence results remain available even if the external destination is unavailable.

## `mcpActionPreset` (type: `string`):

Choose a safe action pattern. AUTO_SAFE_WRITE discovers a compatible non-destructive write tool automatically; use a specific preset for Slack, GitHub, Notion, or database delivery.

## Actor input object example

```json
{
  "records": [
    {
      "platform": "Weibo",
      "contentId": "example-weibo",
      "title": "AI wearable launch",
      "topic": "AI wearable",
      "creator": "Brand Example",
      "engagement": 1400,
      "engagementDelta": 240,
      "rank": 3,
      "rankDelta": 2
    },
    {
      "platform": "RedNote",
      "contentId": "example-rednote",
      "title": "AI wearable hands-on",
      "topic": "AI wearable",
      "creator": "Creator Example",
      "engagement": 1000,
      "engagementDelta": 190,
      "rank": 5,
      "rankDelta": 2
    }
  ],
  "minPlatforms": 1,
  "minMomentumScore": 0,
  "monthlyRuns": 1,
  "mcpToolName": "",
  "mcpToolArguments": {},
  "mcpFailOnError": false,
  "mcpActionPreset": "AUTO_SAFE_WRITE"
}
```

# Actor output Schema

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

Normalized China social trend and KOL signals fused across supplied public-platform snapshots.

# 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 = {
    "records": [
        {
            "platform": "Weibo",
            "contentId": "example-weibo",
            "title": "AI wearable launch",
            "topic": "AI wearable",
            "creator": "Brand Example",
            "engagement": 1400,
            "engagementDelta": 240,
            "rank": 3,
            "rankDelta": 2
        },
        {
            "platform": "RedNote",
            "contentId": "example-rednote",
            "title": "AI wearable hands-on",
            "topic": "AI wearable",
            "creator": "Creator Example",
            "engagement": 1000,
            "engagementDelta": 190,
            "rank": 5,
            "rankDelta": 2
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("quanmatrix/china-cross-platform-trend-kol-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 = { "records": [
        {
            "platform": "Weibo",
            "contentId": "example-weibo",
            "title": "AI wearable launch",
            "topic": "AI wearable",
            "creator": "Brand Example",
            "engagement": 1400,
            "engagementDelta": 240,
            "rank": 3,
            "rankDelta": 2,
        },
        {
            "platform": "RedNote",
            "contentId": "example-rednote",
            "title": "AI wearable hands-on",
            "topic": "AI wearable",
            "creator": "Creator Example",
            "engagement": 1000,
            "engagementDelta": 190,
            "rank": 5,
            "rankDelta": 2,
        },
    ] }

# Run the Actor and wait for it to finish
run = client.actor("quanmatrix/china-cross-platform-trend-kol-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 '{
  "records": [
    {
      "platform": "Weibo",
      "contentId": "example-weibo",
      "title": "AI wearable launch",
      "topic": "AI wearable",
      "creator": "Brand Example",
      "engagement": 1400,
      "engagementDelta": 240,
      "rank": 3,
      "rankDelta": 2
    },
    {
      "platform": "RedNote",
      "contentId": "example-rednote",
      "title": "AI wearable hands-on",
      "topic": "AI wearable",
      "creator": "Creator Example",
      "engagement": 1000,
      "engagementDelta": 190,
      "rank": 5,
      "rankDelta": 2
    }
  ]
}' |
apify call quanmatrix/china-cross-platform-trend-kol-intelligence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,quanmatrix/china-cross-platform-trend-kol-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/lN7eJFvfAvnsJRFJi/builds/RiqfraS9LJ0JlRNAz/openapi.json