Overview
In social media data analysis, understanding both "who is discussing a topic" and "what they are saying" are equally important dimensions. Octoparse provides two Twitter cloud templates that address exactly these two needs:
Twitter Advanced Search Comments Scraper — captures tweet content matching specific search criteria
Twitter People Search Scraper — retrieves basic profile information of users participating in topic discussions
Traditionally, these two tasks would be configured and run separately, with results manually consolidated afterward. With the Bundle capability of Octoparse MCP (Model Context Protocol), you can complete the entire workflow in a single session using natural language instructions, dramatically increasing efficiency.
Before You Start
Before using this workflow, make sure you have the following:
An MCP-enabled client
You need to connect Octoparse through an MCP-compatible client before running this workflow.A paid Octoparse account
Both templates used in this best practice require a paid Octoparse account.Enough Octoparse credits
Make sure your account has sufficient credits before running tasks, especially when scraping multiple authors or accounts in batches.The two required Twitter templates
This workflow uses:A clear target topic or hashtag
Prepare the hashtag or topic you want to monitor before starting.
Important
This workflow will not run properly if:
your MCP connection is not set up
your Octoparse account does not support these templates
your account does not have enough credits
Template Overview
Use case: Intent signal discovery → Prospect identification → Lead pool expansion
Template A
Twitter Advanced Search Comments Scraper
Use this template to collect replies and comments around a target topic, then identify users showing strong interest or buying intent.
Property | Details |
Template ID | 1838 |
Run Mode | Cloud Only |
Pricing | $0.0006 / record |
Core Function | Scrapes replies and comments from Twitter/X search results using advanced queries; extracts tweet text, author, timestamp, and engagement data |
Typical Input | Keywords, hashtags, date range, and other advanced search parameters |
Output Fields | tweet_text, author, timestamp, likes, retweets, replies_count |
Template B
Twitter People Search Scraper
Use this template to expand from the initial signal users and build a broader list of similar people for outreach or lead generation.
Property | Details |
Template ID | 2146 |
Run Mode | Cloud Only |
Pricing | $0.0003 / record |
Core Function | Scrapes user profiles from the Twitter Advanced Search People tab, including username, handle, bio, user type, profile URL, and avatar URL |
Typical Input | Search keywords (consistent with the Comments template) |
Output Fields | user_name, user_handle, bio, user_type, profile_url, avatar_url |
💡 Pro Tip — Three-Template Bundle:
For even richer analysis, add the Twitter Advanced Search Scraper (ID: 288) to your Bundle. It captures full tweet bodies (including likes, retweets, and media) at only $0.0003/record — the most popular Twitter template on Octoparse with 135 likes. Combined with the Comments and People scrapers, you get a complete 360° view: tweets → replies → users, all in one session.
Why Use Bundle?
Running each template individually is already convenient, but Bundle delivers additional efficiency gains:
Comparison | Running Tasks Individually | MCP Bundle |
Steps | Create task → Configure → Start → Download results → Merge manually | One natural language instruction → MCP auto-starts both tasks → Results returned together |
Time Cost | Linear: T1 + T2 | Near max (T1, T2) — tasks run in parallel |
Data Correlation | Manually align data by keyword | Shared context within session enables easy joint analysis |
Error Risk | More manual steps = higher chance of misconfiguration | Unified parameter management ensures consistency |
Best Use Cases
Use Case 1: Brand Sentiment Monitoring
Goal: Understand discussion volume around a brand on Twitter and identify what types of users are spreading related topics.
Bundle Strategy:
Comments Scraper: Collect tweets mentioning the brand; analyze sentiment and high-frequency keywords
People Scraper: Identify highly active users — determine if they are KOLs, media outlets, or regular users
Cross-analysis: Prioritize responding to posts with high engagement authored by industry KOLs
Use Case 2: Competitive Analysis
Goal: Compare discussion quality and user profiles across competitor keywords.
Bundle Strategy:
Create parallel Bundle tasks for each competitor keyword
Use Comments data to analyze user pain points and feature feedback
Use People data to compare core user demographics across competitors
Use Case 3: KOL Discovery & Partnership Evaluation
Goal: Find suitable KOL collaboration candidates within a specific topic space.
Bundle Strategy:
People Scraper: Get a list of active users in the target topic
Comments Scraper: Retrieve high-engagement content
Combine both datasets to filter for KOLs with high content quality and matching audience profiles
# | Tip | Description |
1 | Keep Keywords Consistent | Use identical keywords for both templates to ensure data comes from the same source, enabling reliable cross-analysis. |
2 | Align Time Range Parameters | Comments Scraper supports time filtering. Align it with the People Scraper's data window to avoid temporal mismatch between the two datasets. |
3 | Test with Small Batches First | Before a large-scale scrape, validate your configuration with 50–100 records to confirm expected output and avoid wasting credits. |
4 | Perform Analysis Within the MCP Session | Describe your analysis needs directly in the conversation and let MCP perform initial data correlation — no need to export and process in external tools. |
5 | Save Task IDs | Record the task IDs returned by MCP so you can re-fetch data later without rerunning (and re-billing) the tasks. |
6 | Monitor Account Balance | Both templates are priced per record ($0.0006 and $0.0003). When running Bundle tasks concurrently, estimate total usage in advance. |
Example Prompt
🔍 Check Template Availability
Search for Twitter-related Octoparse templates and confirm that template ID 1838 (Twitter Advanced Search Comments Scraper) and template ID 2146 (Twitter People Search Scraper) are available for my account. Show me their run mode, pricing, and required account level.
🚀 Launch a Bundle Task (Brand Monitoring)
Please run these two Octoparse cloud tasks at the same time:
1. Twitter Advanced Search Comments Scraper (Template ID 1838), keyword: "Tesla 2025", past 7 days, up to 200 records
2. Twitter People Search Scraper (Template ID 2146), same keyword: "Tesla 2025", up to 100 records
When both are done, tell me how many records each task collected.
📊 Check Task Status
What is the current execution status of the two tasks I just launched? Are they still running, completed, or did any of them fail?
📥 Retrieve & Cross-Analyze Results
Both tasks are now complete. Please fetch the first 100 records from the Comments task and the first 50 records from the People task. Then tell me:
- Which users from the People data also appear as authors in the Comments data?
- What are the top 5 most-liked tweets, and who posted them?
- Are any of the overlapping users verified accounts or KOLs based on their bio?
🔁 Competitive Analysis Bundle
Run 4 Octoparse cloud tasks simultaneously — one Comments scrape and one People scrape for each of these two competitor keywords: "OpenAI GPT-5" and "Google Gemini Ultra". Use Template ID 1838 for Comments and Template ID 2146 for People. Limit each task to 100 records. Once all 4 are done, compare which keyword generated more engagement and which attracted more verified users.
🧪 Small Batch Test Before Full Run
Before I run a large scrape, please create a test task using Template ID 1838 with keyword "climate tech", limited to 50 records only. I want to verify the output fields and data quality before committing to a full batch.
Summary
The Bundle capability of Octoparse MCP compresses what used to be a multi-step manual data collection process into a single natural language conversation. For social platforms like Twitter — where both content and user context matter — combining the Twitter Advanced Search Comments Scraper with the Twitter People Search Scraper delivers a richer, multi-dimensional data perspective in far less time:
Content Layer: Understand discussion quality and sentiment through comment data
User Layer: Understand the identity composition of participants through People Search data
Cross Layer: Perform preliminary two-dimensional correlation analysis directly within the MCP session
🚀 Get Started
Once Octoparse MCP Server is configured in your AI client, a single instruction is all it takes to launch a Bundle task. No code required, no interface switching — bring data collection directly into your analysis workflow.