Twitter User Following Scraper

The Twitter User Following Scraper helps you quickly collect detailed information about the accounts a Twitter user follows. 🐦 It simplifies data collection, saves time, and delivers structured results for research, analysis, and growth strategies. Perfect for marketers, analysts, and researchers seeking clear audience insights. 📊

🐦 What does Twitter User Following Scraper do?

The Twitter User Following Scraper is a powerful tool designed to collect detailed data about the accounts a specific Twitter (X) user is following. 🚀 Instead of manually scrolling through profiles, this scraper automatically gathers structured information at scale, saving hours of effort.

With the Twitter User Following Scraper, you can quickly understand user interests, networking patterns, and influencer connections. 💡 It works seamlessly alongside popular solutions like apify twitter profile scraper, apidojo twitter user scraper, and even community tools such as twitter followers scraper github projects.

Whether you are analyzing competitors, building audience insights, or tracking niche communities, this scraper ensures accurate, clean, and well-organized data. 📊 It also complements tools like twitter media scraper, twitter image scraper, and ai twitter scraper for broader data collection needs.

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📊 What Twitter User Following Scraper can I extract?

The Twitter User Following Scraper extracts a wide range of valuable user-level details. Below is a clear overview of the data you can collect:

📌 Data Type	📄 Description
Username	Unique Twitter handle of followed users
Display Name	Public profile name
User ID	Platform-specific unique identifier
Bio	Profile description text
Profile Image	Avatar image URL (works with twitter image scraper)
Followers Count	Total followers number
Following Count	Number of accounts followed
Verified Status	Blue-check verification indicator
Account URL	Direct profile link

This data is extremely useful for audience mapping, influencer discovery, and account analysis. 🔍 Many users combine it with twitter scraper github utilities or tools like apify twitter profile scraper to enrich datasets. The structured output also integrates well with dashboards, spreadsheets, and reporting workflows.

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✨ Key Features of Twitter User Following Scraper

• ⚡ Fast & scalable scraping for small or large accounts

• 🧩 Structured output for easy analysis and reporting

• 🤖 Compatible with ai twitter scraper workflows

• 🖼️ Extracts media-ready data (pairs well with twitter media scraper)

• 🔗 Works alongside apidojo twitter user scraper ecosystems

• 📁 Export-ready formats (JSON, CSV, Excel)

• 🛠️ No manual scrolling or copying required

• 🌍 Suitable for research, marketing, and monitoring use cases

These features make the Twitter User Following Scraper a reliable alternative to many twitter followers scraper github repositories while offering better consistency and clarity. ✅

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🧭 How to use Twitter User Following Scraper

Using the Twitter User Following Scraper is simple and beginner-friendly. Follow this step-by-step guide 👇

1. Log in to Apify 🔐
   Create a free account or sign in to your existing dashboard.

2. Select the Actor 🧩
   Search for “Twitter User Following Scraper” in the Apify Store.

3. Enter Input Data ✍️
   Paste the Twitter profile URL(s) into the startUrls field.

4. Choose Options ⚙️
   Set limits, filters, or output preferences based on your needs.

5. Run the Actor ▶️
   Click start, and the scraper will begin collecting following data automatically.

6. Download Results 📥
   Export results in JSON, CSV, or Excel format for further use.

This workflow is similar to popular tools like apify twitter profile scraper and easy to integrate with twitter scraper github pipelines.

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🎯 Use Cases

The Twitter User Following Scraper fits multiple real-world scenarios:

• 📈 Market research – Understand who competitors and influencers follow

• 👥 Audience analysis – Map interest clusters and communities

• 🤝 Influencer discovery – Identify relevant creators and collaborators

• 📰 Content research – Spot trends via followed accounts

• 🧠 Academic studies – Analyze social graph behavior

• 🖼️ Media insights – Combine with twitter media scraper or twitter image scraper

Businesses often pair it with ai twitter scraper tools to automate insights and reporting at scale.

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💎 Why choose us?

• ✅ Reliable and accurate results every run

• ⚡ High performance even with large profiles

• 🔄 Easy integration with apidojo twitter user scraper workflows

• 📊 Clean data compatible with analytics tools

• 🛡️ Built with stability and scalability in mind

• 🌍 Trusted by researchers, marketers, and analysts

Unlike many experimental twitter followers scraper github projects, this solution is production-ready and consistent. 🌟

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📦 How many results can you scrape with Twitter User Following Scraper?

The Twitter User Following Scraper is designed to handle both small and large-scale data collection efficiently. 🚀 You can scrape:

• 🔹 Dozens of followed accounts from niche users

• 🔹 Thousands of accounts from influencers or brands

• 🔹 Multiple profiles in a single run

There is no hard-coded limit on results; scalability depends on input configuration and runtime preferences. 📈 This flexibility makes it ideal for long-term projects, recurring data pulls, and bulk analysis.

Many users transitioning from twitter scraper github tools appreciate the stability and performance improvements. When combined with apify twitter profile scraper or ai twitter scraper solutions, it supports advanced automation pipelines without manual intervention.

In short, whether you need quick insights or massive datasets, the Twitter User Following Scraper adapts smoothly to your workload.

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⚖️ Is it legal to scrape Twitter User Following Scraper data?

Scraping publicly available Twitter profile data is generally acceptable when done responsibly. ✅ The Twitter User Following Scraper only accesses information that users choose to make public.

However, it’s important to:

• 📜 Review platform terms and regional regulations

• 🔐 Avoid collecting private or sensitive data

• 🧩 Use scraped data ethically and transparently

When used properly, this scraper is suitable for research, analysis, and business intelligence purposes. Always ensure compliance with applicable rules before large-scale usage.

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🧾 Input Parameters

🔹 Example Input JSON

{
  "startUrls": [
    "jordanbpeterson",
    "https://x.com/elonmusk",
    "twitter.com/billgates"
  ],
  "maxResults": 100,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}

📤 Output Format

🔹 Example Output JSON

{
  "username": "jordanbpeterson",
  "id": "17995040",
  "name": "Ben Shapiro",
  "screen_name": "benshapiro",
  "profile_image_url_https": "https://pbs.twimg.com/profile_images/...",
  "profile_banner_url": "https://pbs.twimg.com/profile_banners/...",
  "description": "Daily Wire co-founder, host of @benshapiroshow...",
  "followers_count": 8010425,
  "friends_count": 486,
  "statuses_count": 204599,
  "is_blue_verified": true,
  "verified_type": null,
  "created_at": "Tue Dec 09 16:40:22 +0000 2008",
  "profile_url": "https://x.com/benshapiro",
  "location": "",
  "url": "https://t.co/kvSlznhfDe",
  "favourites_count": 50,
  "media_count": 8629,
  "listed_count": 18882,
  "fast_followers_count": 0,
  "normal_followers_count": 8010425,
  "profile_image_shape": "Circle"
}

❓ FAQ

1️⃣ What is Twitter User Following Scraper used for?

It collects data about the accounts a user follows for analysis and research.

2️⃣ Can it replace twitter followers scraper github tools?

Yes, it offers more stability and structured outputs.

3️⃣ Does it work with apify twitter profile scraper?

Absolutely, both tools complement each other.

4️⃣ Can I extract images?

Yes, it pairs well with twitter image scraper workflows.

5️⃣ Is it suitable for automation?

Yes, many users integrate it with ai twitter scraper systems.

6️⃣ Can beginners use it?

Yes, the setup is simple and user-friendly. 😊

✅ Conclusion

The Twitter User Following Scraper is a reliable solution for extracting accurate following data at any scale. 🚀 With flexible inputs, clean outputs, and smooth integration options, it supports smarter decisions, deeper audience understanding, and efficient workflows for long-term Twitter data projects. 💡