# 🧪 China-to-West Trend Timing Evidence (`thenetaji/douyin-rednote-weibo-trend-migration-evidence`) Actor Compare explicit topic aliases across Douyin, RedNote, Weibo, TikTok, Instagram, and YouTube with dated observations and observed timing for market comparison. - **URL**: https://apify.com/thenetaji/douyin-rednote-weibo-trend-migration-evidence.md - **Developed by:** [The Netaji](https://apify.com/thenetaji) (community) - **Categories:** Social media, Marketing, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $8.00 / 1,000 topic timing checks 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-to-West Trend Timing Evidence Check explicit topic aliases across Douyin, RedNote, Weibo, TikTok, Instagram, and YouTube. Each result keeps exact-match records, earliest dated observations, coverage gaps, and observed timing between markets. ### Accepted input `trendTopics` is a JSON array. Every topic needs a name and at least one explicit alias. Use `platformAliases` when wording differs by language or platform. ```json { "trendTopics": [ { "name": "Matcha drinks", "aliases": ["matcha"], "platformAliases": { "douyin": ["抹茶"], "rednote": ["抹茶"], "weibo": ["抹茶"], "tiktok": ["matcha drinks"] } } ], "sourcePageLimit": 1, "trendEvidenceLimit": 10, "maxItems": 20 } ``` Shared aliases are checked on every selected platform. Platform-specific aliases are checked only on that platform. Matching ignores case and punctuation; aliases are not translated or expanded into related concepts. ### Response fields Each row represents one submitted topic: - `topic` contains its name and explicit aliases. - `china` and `west` contain exact-match counts and the earliest dated observation found on each side. - `observations` preserves bounded matching records with platform, submitted alias, timestamp, source ID or URL, and source text. - `lag` reports the observed direction and day difference when both sides have dated evidence. - `corroboration_status` distinguishes cross-market evidence, one-sided evidence, and no evidence. - `claims` keeps origin, causality, and market-migration conclusions disabled. - `analysis` and `provenance` record evidence coverage and collection outcomes. ```json { "record_type": "trend_corroboration", "topic": { "name": "Matcha drinks", "aliases": ["matcha", "抹茶"] }, "china": { "evidence_count": 4, "first_observed_at": "2026-01-01T00:00:00.000Z" }, "west": { "evidence_count": 3, "first_observed_at": "2026-01-05T00:00:00.000Z" }, "lag": { "status": "west_observed_after_china", "days": 4, "direction": "china_to_west_order" }, "corroboration_status": "cross_market_observed", "claims": { "origin_supported": false, "causality_supported": false, "market_migration_supported": false } } ``` ### Evidence boundary Timestamps show when matching records appeared in the returned sample. They do not show when a trend began, and the sample may change with platform coverage and availability. The `china` group means Douyin, RedNote, and Weibo. The `west` group means TikTok, Instagram, and YouTube. These groups describe platform evidence, not the location or nationality of an author or viewer. `west_observed_after_china` means only that the earliest dated China record in this run precedes the earliest dated Western record in this run. It is not evidence that the topic originated in China, caused later posts, or migrated between markets. An undated record contributes to counts but not lag. ### Partial results Platforms are evaluated independently. A failed platform produces a partial row while successful observations remain available. An empty result is a valid timing check with `insufficient_evidence`. # Actor input Schema ## `trendTopics` (type: `array`): Topics to check with explicit aliases. Use platformAliases when the wording differs by language or platform. ## `sourcePageLimit` (type: `integer`): Maximum pages checked for each alias on each platform. ## `trendEvidenceLimit` (type: `integer`): Maximum unique exact-match observations retained from each platform across the topic's submitted aliases. ## `maxItems` (type: `integer`): Maximum topic corroboration rows to save. Set 0 for no limit. ## Actor input object example ```json { "trendTopics": [ { "name": "Matcha drinks", "aliases": [ "matcha" ], "platformAliases": { "douyin": [ "抹茶" ], "rednote": [ "抹茶" ], "weibo": [ "抹茶" ] } } ], "sourcePageLimit": 1, "trendEvidenceLimit": 10, "maxItems": 5 } ``` # Actor output Schema ## `dataset` (type: `string`): All records scraped by this run # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "trendTopics": [ { "name": "Matcha drinks", "aliases": [ "matcha" ], "platformAliases": { "douyin": [ "抹茶" ], "rednote": [ "抹茶" ], "weibo": [ "抹茶" ] } } ], "sourcePageLimit": 1, "trendEvidenceLimit": 10, "maxItems": 5 }; // Run the Actor and wait for it to finish const run = await client.actor("thenetaji/douyin-rednote-weibo-trend-migration-evidence").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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "trendTopics": [{ "name": "Matcha drinks", "aliases": ["matcha"], "platformAliases": { "douyin": ["抹茶"], "rednote": ["抹茶"], "weibo": ["抹茶"], }, }], "sourcePageLimit": 1, "trendEvidenceLimit": 10, "maxItems": 5, } # Run the Actor and wait for it to finish run = client.actor("thenetaji/douyin-rednote-weibo-trend-migration-evidence").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 '{ "trendTopics": [ { "name": "Matcha drinks", "aliases": [ "matcha" ], "platformAliases": { "douyin": [ "抹茶" ], "rednote": [ "抹茶" ], "weibo": [ "抹茶" ] } } ], "sourcePageLimit": 1, "trendEvidenceLimit": 10, "maxItems": 5 }' | apify call thenetaji/douyin-rednote-weibo-trend-migration-evidence --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com/?tools=fetch-actor-details,thenetaji/douyin-rednote-weibo-trend-migration-evidence" } } } ``` 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 ` 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/4poorpatX7rNvEBcS/builds/f26LhgrmLh1QhkY3E/openapi.json