# WeChat Article Repost & Original Source Tracker (`scrupulous_buckler/wechat-article-repost-original-source-tracker`) Actor Detect similar WeChat Official Account articles, infer the earliest source in the collected sample, reconstruct publisher sequence, and return evidence-linked repost clusters. Live public search or your own datasets. - **URL**: https://apify.com/scrupulous\_buckler/wechat-article-repost-original-source-tracker.md - **Developed by:** [Peng Lyu](https://apify.com/scrupulous_buckler) (community) - **Categories:** Social media - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.99 / repost and original-source report 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use 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 ## WeChat Article Repost & Original Source Tracker Find similar WeChat Official Account articles, group probable reposts, infer the earliest source visible in the collected sample, and reconstruct the publisher sequence with timestamps, similarity scores, and evidence links. **Unofficial tool. This Actor is not affiliated with, sponsored by, or endorsed by Tencent, WeChat, Weixin, Sogou, or any source platform.** ### What buyers receive - similar-title and similar-content article clusters - a likely-original article for each cluster - explicit high, medium, or low original-source confidence - publisher-by-publisher publication sequence - time lag from the likely original to each possible repost - title and available snippet/body similarity scores - publisher propagation flows across multiple clusters - unmatched articles for coverage auditing - evidence URLs and data-quality warnings This Actor does not call a similarity signal proof of authorship, plagiarism, copyright ownership, or unlawful copying. “Likely original” means the earliest timestamp visible inside the supplied or collected sample. ### Data sources 1. **Static preview** — synthetic articles demonstrate two repost clusters without triggering the $2.99 custom report event. 2. **Live public search** — discover public articles through a separate Sogou WeChat search Actor. 3. **Apify Datasets** — analyze up to ten existing article datasets. 4. **Uploaded records** — paste up to 100 customer-owned article records. Common article title, snippet, body, publisher, timestamp, article URL, Sogou URL, keyword, and rank aliases are normalized automatically. ### Pricing One successful live, Dataset, or uploaded-data report costs **$2.99** and analyzes up to **100 unique articles**. The built-in static preview does not trigger the custom report event; Apify may still show the small Actor-start or platform usage charge. Fewer than two usable articles do not create a paid report. Live search invokes a separate public upstream Actor whose usage is billed separately by Apify. Uploaded and Dataset modes do not invoke a collector. ### Important limits - Search coverage is incomplete and can omit an earlier source. - Equal or missing timestamps reduce original-source confidence. - Snippet-only records are weaker evidence than full article text. - Edited, translated, licensed, syndicated, or independently similar content can produce similarity signals. - Review evidence before making legal, enforcement, or attribution decisions. ### Example ```json { "mode": "liveSearch", "keywords": ["品牌发布会", "低糖早餐选购指南"], "maxResults": 100, "similarityThreshold": 0.58, "titleWeight": 0.65 } ```` ### Local validation ```bash python3 -m unittest discover -s tests -v ``` # Actor input Schema ## `mode` (type: `string`): Try the sample, search public articles, analyze Apify Datasets, or paste your own article records. ## `keywords` (type: `array`): One to twenty brand, campaign, topic, or distinctive headline phrases. Required only in live-search mode. ## `sourceDatasetIds` (type: `array`): Up to ten datasets containing WeChat article titles, snippets or full text, publishers, timestamps and evidence URLs. ## `uploadedRecords` (type: `array`): Paste JSON article records. Common English, Chinese and Sogou field aliases are recognized. ## `maxResults` (type: `integer`): One $2.99 report analyzes up to 100 unique article records. ## `similarityThreshold` (type: `number`): Lower values find looser rewrites; higher values keep only closer title/content matches. The default is conservative enough for discovery, not legal proof. ## `titleWeight` (type: `number`): Weight assigned to normalized title overlap; the remaining weight uses available snippet/body text. ## `includeNonMatched` (type: `boolean`): Keeps the complete analyzed sample auditable alongside matched clusters. ## Actor input object example ```json { "mode": "demo", "keywords": [], "sourceDatasetIds": [], "uploadedRecords": [], "maxResults": 100, "similarityThreshold": 0.58, "titleWeight": 0.65, "includeNonMatched": true } ``` # Actor output Schema ## `report` (type: `string`): No description ## `articles` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("scrupulous_buckler/wechat-article-repost-original-source-tracker").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("scrupulous_buckler/wechat-article-repost-original-source-tracker").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 '{}' | apify call scrupulous_buckler/wechat-article-repost-original-source-tracker --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrupulous_buckler/wechat-article-repost-original-source-tracker", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "WeChat Article Repost & Original Source Tracker", "description": "Detect similar WeChat Official Account articles, infer the earliest source in the collected sample, reconstruct publisher sequence, and return evidence-linked repost clusters. Live public search or your own datasets.", "version": "0.1", "x-build-id": "yuMIKFg4Ab3c2xlJf" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrupulous_buckler~wechat-article-repost-original-source-tracker/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrupulous_buckler-wechat-article-repost-original-source-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrupulous_buckler~wechat-article-repost-original-source-tracker/runs": { "post": { "operationId": "runs-sync-scrupulous_buckler-wechat-article-repost-original-source-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrupulous_buckler~wechat-article-repost-original-source-tracker/run-sync": { "post": { "operationId": "run-sync-scrupulous_buckler-wechat-article-repost-original-source-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "mode": { "title": "Data source", "enum": [ "demo", "liveSearch", "dataset", "uploadedRecords" ], "type": "string", "description": "Try the sample, search public articles, analyze Apify Datasets, or paste your own article records.", "default": "demo" }, "keywords": { "title": "Article discovery keywords", "type": "array", "description": "One to twenty brand, campaign, topic, or distinctive headline phrases. Required only in live-search mode.", "default": [], "items": { "type": "string" } }, "sourceDatasetIds": { "title": "Apify Dataset IDs", "type": "array", "description": "Up to ten datasets containing WeChat article titles, snippets or full text, publishers, timestamps and evidence URLs.", "default": [], "items": { "type": "string" } }, "uploadedRecords": { "title": "Uploaded article records", "type": "array", "description": "Paste JSON article records. Common English, Chinese and Sogou field aliases are recognized.", "default": [] }, "maxResults": { "title": "Articles to analyze (maximum 100)", "minimum": 2, "maximum": 100, "type": "integer", "description": "One $2.99 report analyzes up to 100 unique article records.", "default": 100 }, "similarityThreshold": { "title": "Repost similarity threshold", "minimum": 0.35, "maximum": 0.95, "type": "number", "description": "Lower values find looser rewrites; higher values keep only closer title/content matches. The default is conservative enough for discovery, not legal proof.", "default": 0.58 }, "titleWeight": { "title": "Title contribution to similarity", "minimum": 0.3, "maximum": 0.9, "type": "number", "description": "Weight assigned to normalized title overlap; the remaining weight uses available snippet/body text.", "default": 0.65 }, "includeNonMatched": { "title": "Include articles with no detected repost match", "type": "boolean", "description": "Keeps the complete analyzed sample auditable alongside matched clusters.", "default": true } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```