# Zhihu & Douban High-Intent Research (`scrupulous_buckler/zhihu-douban-high-intent-research`) Actor Turn Zhihu and Douban discussions into evidence-linked decision journeys, objections, proof requirements, alternatives, competitor associations, and content opportunities. - **URL**: https://apify.com/scrupulous\_buckler/zhihu-douban-high-intent-research.md - **Developed by:** [Peng Lyu](https://apify.com/scrupulous_buckler) (community) - **Categories:** AI, Social media, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.99 / full intent research 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 a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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 ## Zhihu & Douban High-Intent Research Turn authorized Zhihu and Douban discussions into evidence-linked consumer decision research for health, supplements, education, finance, durable goods, professional services, and other high-consideration categories. **Unofficial tool. This Actor is not affiliated with, sponsored by, or endorsed by Zhihu, Douban, or any source platform.** ### Why this is different from a scraper Raw rows do not answer why someone buys, delays, rejects, switches, or searches for an alternative. This Actor maps supplied posts, answers, reviews, threads, comments, and replies into: - ranked high-intent questions with transparent scores and reasons - problem recognition, option exploration, comparison and proof, purchase, and post-purchase journey stages - objections about outcomes, safety, trust, price, effort, and after-sales risk - proof requirements such as real experience, expert explanation, research, comparison, failure cases, and total cost - product and non-product alternatives - evidence-linked brand and competitor associations - Zhihu versus Douban discussion differences - ranked content opportunities tied to observed decision gaps - risky-claim screening for medical, health, finance, employment, and education promises ### Private-data-safe input No Cookie is required or requested. Paste records into `uploadedRecords`, or provide `sourceDatasetIds` for private Apify datasets explicitly supplied to the run. The Actor uses Limited permissions and cannot browse unrelated account data. ### Modes - `demo`: ten-record health-category example across Zhihu and Douban - `intentQuestions`: high-intent questions, objections, and proof requirements - `decisionJourney`: decision stages and the strongest source evidence - `competitorResearch`: competitors, alternatives, associations, and platform differences - `contentOpportunities`: ranked decision-stage content opportunities - `fullReport`: every module in one report ### Example ```json { "mode": "fullReport", "topic": "成人正畸", "knownEntities": [ "隐形矫正", "金属托槽", "公立医院", "私立诊所", "品牌甲", "品牌乙" ], "competitorNames": ["品牌甲", "品牌乙"], "regulatedTopic": true, "uploadedRecords": [ { "id": "zh-1", "platform": "zhihu", "recordType": "post", "title": "成人正畸怎么选", "text": "更关心医生经验、失败案例、总费用和保持器。", "url": "https://example.com/source/1" }, { "id": "db-1", "platform": "douban", "recordType": "comment", "text": "做完之后后悔吗？长期复盘和售后怎么样？", "url": "https://example.com/source/2" } ] } ```` ### Pricing | Report | Price | | --- | ---: | | High-intent questions or decision journey | $1.99 | | Competitor and alternative research | $2.49 | | Content opportunities | $1.99 | | Full intent research | $4.99 | Platform usage is included in the report price. ### Transparent scoring The high-intent score uses explicit signals: question language, decision stage, objections, proof requirements, alternatives, and whether the record is a comment/reply. The output explains every score. It does not infer private health, income, education, or demographic traits. ### Analytical boundaries - Results describe the supplied sample, not all Zhihu or Douban users. - Zhihu and Douban counts remain separate; raw engagement is not compared as if the platforms were equivalent. - Consumer stories are evidence of discussion, not clinical, financial, legal, education, or product-performance proof. - Regulated-claim screening is a research aid, not compliance approval. ### Responsible use Only process data you are authorized to use. Follow platform terms, privacy, advertising, intellectual-property, and data-protection rules. Do not use the output for harassment, sensitive profiling, diagnosis, or unsupported promises. ### Local validation ```bash python3 -m unittest discover -s tests -v ``` # Actor input Schema ## `mode` (type: `string`): Run the demo or choose a focused or full decision-research report. ## `topic` (type: `string`): For example: blood glucose supplements, international school selection, adult orthodontics, insurance, or electric vehicles. ## `uploadedRecords` (type: `array`): Paste posts, answers, reviews, threads, comments, or replies. Common scraper field aliases are recognized. ## `sourceDatasetIds` (type: `array`): Optional private datasets explicitly supplied to this run. Limited permissions prevent unrelated account-data access. ## `knownEntities` (type: `array`): Names whose associations and comparison roles should be measured. ## `competitorNames` (type: `array`): Two to ten named brands, products, services, schools, treatments, or solutions. ## `regulatedTopic` (type: `boolean`): Screens risky medical, health, finance, and guaranteed-outcome language. This is not legal or clinical approval. ## `minimumIntentScore` (type: `integer`): Only records at or above this transparent score enter the high-intent question list. ## `maxEvidence` (type: `integer`): Limits source-linked evidence in questions, objections, entities, and journey stages. ## `maxOpportunities` (type: `integer`): Maximum ranked evidence-backed opportunities. ## `maxRecords` (type: `integer`): Hard total across uploads and private datasets. ## Actor input object example ```json { "mode": "demo", "uploadedRecords": [], "sourceDatasetIds": [], "knownEntities": [], "competitorNames": [], "regulatedTopic": false, "minimumIntentScore": 45, "maxEvidence": 20, "maxOpportunities": 10, "maxRecords": 5000 } ``` # Actor output Schema ## `reports` (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/zhihu-douban-high-intent-research").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/zhihu-douban-high-intent-research").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/zhihu-douban-high-intent-research --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrupulous_buckler/zhihu-douban-high-intent-research", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Zhihu & Douban High-Intent Research", "description": "Turn Zhihu and Douban discussions into evidence-linked decision journeys, objections, proof requirements, alternatives, competitor associations, and content opportunities.", "version": "0.1", "x-build-id": "qPxfhhykBRAd2j3df" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrupulous_buckler~zhihu-douban-high-intent-research/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrupulous_buckler-zhihu-douban-high-intent-research", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrupulous_buckler~zhihu-douban-high-intent-research/runs": { "post": { "operationId": "runs-sync-scrupulous_buckler-zhihu-douban-high-intent-research", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrupulous_buckler~zhihu-douban-high-intent-research/run-sync": { "post": { "operationId": "run-sync-scrupulous_buckler-zhihu-douban-high-intent-research", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Research mode", "enum": [ "demo", "intentQuestions", "decisionJourney", "competitorResearch", "contentOpportunities", "fullReport" ], "type": "string", "description": "Run the demo or choose a focused or full decision-research report.", "default": "demo" }, "topic": { "title": "Category or decision topic", "type": "string", "description": "For example: blood glucose supplements, international school selection, adult orthodontics, insurance, or electric vehicles." }, "uploadedRecords": { "title": "Zhihu and Douban records", "type": "array", "description": "Paste posts, answers, reviews, threads, comments, or replies. Common scraper field aliases are recognized.", "default": [] }, "sourceDatasetIds": { "title": "Private Apify Dataset IDs", "type": "array", "description": "Optional private datasets explicitly supplied to this run. Limited permissions prevent unrelated account-data access.", "default": [], "items": { "type": "string" } }, "knownEntities": { "title": "Brands, products, services, ingredients, and alternatives", "type": "array", "description": "Names whose associations and comparison roles should be measured.", "default": [], "items": { "type": "string" } }, "competitorNames": { "title": "Competitors to compare", "type": "array", "description": "Two to ten named brands, products, services, schools, treatments, or solutions.", "default": [], "items": { "type": "string" } }, "regulatedTopic": { "title": "Regulated or high-consequence topic", "type": "boolean", "description": "Screens risky medical, health, finance, and guaranteed-outcome language. This is not legal or clinical approval.", "default": false }, "minimumIntentScore": { "title": "Minimum high-intent score", "minimum": 20, "maximum": 90, "type": "integer", "description": "Only records at or above this transparent score enter the high-intent question list.", "default": 45 }, "maxEvidence": { "title": "Maximum evidence rows per section", "minimum": 5, "maximum": 100, "type": "integer", "description": "Limits source-linked evidence in questions, objections, entities, and journey stages.", "default": 20 }, "maxOpportunities": { "title": "Maximum content opportunities", "minimum": 1, "maximum": 50, "type": "integer", "description": "Maximum ranked evidence-backed opportunities.", "default": 10 }, "maxRecords": { "title": "Maximum records to analyze", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Hard total across uploads and private datasets.", "default": 5000 } } }, "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 } } } } } } } } } } ```