LinkedIn Scraper Tutorial: Your Complete Guide to Getting Started

LinkedIn features crucial information such as user profiles, job insights, company insights, and more. You could use this information in multiple ways, be it looking for leads, researching market trends, hiring talent, etc.

While LinkedIn gives you the flexibility to do all these on its platform, collecting large sets of data for personal or business needs isn’t an option. In such a scenario, manually collecting and analyzing the data can be incredibly time-consuming and tiring.

In such a scenario, scraping through automated tools or scripts can be your best pick. However, according to the analysis aggregated by Ping Proxies, LinkedIn is against this process. In this guide, I will present everything needed to scrape, including techniques, tools, and best practices, while respecting LinkedIn guidelines.

What is LinkedIn Scraping?

LinkedIn scraping involves the process of extracting publicly available information through the use of scrapers. Based on our implemented research, mostly the structured information from profiles, company pages, job postings, and other crucial data from accessible sections are often scraped.

Wondering how scraping works with LinkedIn? It’s the same as on other websites. The scraper starts by mimicking human browsing behavior to access and extract specific data points. Next, the data is organized into a structured format, like spreadsheets or databases, so it is easier to analyze and use.

Note: While LinkedIn scraping is widely practiced, it’s essential to approach it responsibly. Scraping should focus only on public data while respecting LinkedIn’s Terms of Service to maintain ethical boundaries.

Benefits of Using LinkedIn Data

Individuals and businesses benefit from the data LinkedIn holds. It is loaded with valuable information that can be used for various purposes, which usually LinkedIn hides behind or provides partial access to. Here are some key benefits:

• Identify Key Players: LinkedIn profiles provide job titles, company names, and industries. If you’re managing a business, you can benefit from finding relevant professionals, whether for recruiting or building partnerships.
• Understand Market Trends: By analyzing LinkedIn data, you can spot patterns such as skills, job roles, or hiring trends in specific industries. Individuals can use these to upskill or switch roles, while businesses can shift strategy or training initiatives.
• Enhance Recruitment Processes: If you’re trying to find the perfect candidates, LinkedIn data helps filter through thousands of profiles. Using the data, you can target individuals based on specific skills, experience levels, and even certifications.
• Keep Contacts Updated: Exporting LinkedIn data into a CRM keeps your contact list updated. This eliminates the need to manually update information like email addresses, job roles, or company changes.
• Personalize Your Communication: Having access to LinkedIn data helps you realize a person's current role or recent achievements. Based on it, you can write better emails or messages to grab attention and boost response rates.
• Build Better Campaigns: It isn’t limited to personalized messages or emails, you can also create targeted marketing campaigns based on your audience’s job roles, industries, and interests.
• Simplify Competitor Research: LinkedIn data is very useful for businesses conducting competitor research. It features company profiles with information about size, hiring trends, or skill sets, helping businesses better understand the market landscape.

Tools for Scraping LinkedIn

Extracting large sets of data manually from LinkedIn is tedious and inefficient. Tools designed for LinkedIn scraping get you over this hassle. They automate the process, save time, and provide structured data while adhering to LinkedIn guidelines when used properly.

According to the analysis aggregated by Ping Proxies, there are multiple ways to scrape LinkedIn, each varying according to purpose and need. Hence, I presented different types of tools and their capabilities to help you make an informed decision.

Chrome Extension for LinkedIn Email Scraping

If you’re targeting multiple leads and manually collecting the emails from their profiles, it's inefficient. In such a case, using LinkedIn Email Scraping tools designed for browsers is the best approach as they automate the process.

It provides verified email addresses directly from profiles or search results and also integrates with CRM and other tools. Based on our research, multiple Chrome LinkedIn email scraping extensions with different capabilities exist. Let’s have a quick look at a few.

Note: All Chrome LinkedIn email scraping tools rely on the LinkedIn sales navigator. Hence, to use these extensions to the full extent, make sure you have an active account.

Scrupp

Scrupp performs LinkedIn data extraction by building detailed lead lists from LinkedIn. It allows you to scrape large batches of leads and filter high-value prospects. Plus, you get to clean extra symbols from fields for better data clarity.

Additionally, Scrupp finds verified professional email addresses, making it a useful option for B2B outreach. You can export data in CSV format or integrate it with CRM tools to keep workflows efficient and organized.

Free Trial: Yes, 100 Scrupp Credits

Starting Price: $19/month (1000 scrapings)

Skrapp.io

Skrapp.io allows you to search and find professional email addresses based on names and companies. It also helps organize data by exporting it into spreadsheets or directly integrating it with CRMs and email marketing platforms.

The unique offering of Skrapp.io is its email verification feature. You can validate the email, leaving you the deliverable contacts for outreach. Its ability to connect with business profiles and email lists makes it a useful addition to lead-generation efforts.

Free Trial: Yes, 100 Skrapp Credits

Starting Price: $19/month (1000 scrapings)

Kaspr

Kaspr's Chrome extension helps you extract and update your contact lists from LinkedIn. With its bulk upload options, you can gather details like email addresses, phone numbers, and company information at once, saving time.

The best part is you can send also this data directly to your favorite CRM or outreach tools, keeping your workflows simple. For advanced users, Kaspr's API gives you the ability to pull contact details straight from LinkedIn URLs and integrate them into your systems.

Free Trial: Yes, 15 B2B email credits

Starting Price: €45/month (Unlimited B2B email credits)

Evaboot

Evaboot helps you quickly turn your searches into organized lead lists you can use right away. With its ability to export leads to a CSV, you can save hours of manual effort. It cleans up names, job titles, and company details automatically, so you don’t have to.

Plus, you can also filter your results to make sure they match the criteria you’re looking for. This way, you can find professional email addresses with ease. The best part is every email is verified, and you don’t have to worry about bounce, as they are ready for outreach.

Free Trail: No

Starting Price: $9/month (100 credits)

Wiza

With Wiza, you can extract professional emails, personal emails, phone numbers, and additional details. The best part is its built-in email verification, which reduces bounce and saves time by focusing only on accurate, deliverable contact information.

The Chrome extension supports exporting data directly into CSV files. Also, you get the flexibility to integrate with CRMs for easy follow-up and outreach. This way, you can transform your LinkedIn activity into useful connections.

Free Trial: Yes, 20 emails per month

Starting Price: $49/month (100 emails)

Overview of LinkedIn Scraper Tools

While Chrome extensions may work for quick LinkedIn data extractions, they can turn inefficient for large tasks. When done improperly, there is a high chance that your LinkedIn account will be banned temporarily or permanently.

In such a scenario, LinkedIn scraper tools are your best pick. They offer features like proxy rotation and advanced filtering, making them a safer and more reliable choice for extracting detailed information. Plus, most automate the scraping, saving you time and effort while reducing the risks.

Our data suggests that multiple LinkedIn scrapers have been developed for different purposes and needs. It is your turn to choose the one that best fits your needs. If you aren’t aware, check out the different tools developed for the LinkedIn job and profile scraping for an idea.

Bright Data

Bright Data is widely recognized for its various proxies, which are suitable for various purposes. Beyond proxies, it offers an in-house scraping browser and scraping automation tools, including one for LinkedIn.

The best part is the LinkedIn dataset marketplace, which comes in handy for those who don't want the hassle of scraping, thanks to the ready-to-use data. Let’s dive into the key features of its LinkedIn scraper.

Key Features:

• Collect data such as profiles, posts, companies, and job details through an API designed for automated and customized scraping.
• The No-Code Scraper simplifies the scraping process, requiring minimal technical expertise.
• Handle large-scale scraping jobs efficiently with built-in automation and scheduling capabilities.
• Retrieve results in multiple formats like JSON or CSV, ready for integration with your workflow.
• Use modern residential proxies to overcome geo-restrictions and maintain data quality.

Free Trial: Yes (Depends on the plan selected).

Starting Price: It operates on a Pay-as-you-go pricing structure ($1.5/1K records). Subscriptions are also available, starting with the Growth plan at $499, which costs $1.27 for 1K records.

Nimble Way

Nimble Way is known for its Web API which is often used for different scraping tasks. It is backed by batch processing capabilities, so you get the flexibility to scrape multiple URLs at once. The best part is that you get built-in residential proxies and AI-powered unblocking, making it ideal when scraping complex websites.

Nimble Way’s LinkedIn API is part of its Web API solutions, and here’s why it's considered for extracting actionable insights from LinkedIn data.

Key Features:

• Access residential proxies with precise geotargeting and high stability for seamless scraping.
• Reliable IPs to handle long-running tasks without interruptions.
• Automated data parsing makes sure you get clean and structured results delivered directly to your storage.
• Nimble AI optimizes millions of IPs to guarantee high availability and performance.

Free Trial: Yes (Varies based on Plan).

Starting Price: It operates on a Pay-as-you-go pricing structure ($3/CPM). Subscriptions are also available, starting with the Beginner plan at $150 for 150 credits.

Apify

With Apify, you get a vast collection of ready-made, pre-configured scrapers for various websites, including LinkedIn. Apify calls them agents, and they are designed to save time by eliminating technical setup and can be customized to meet your unique data extraction needs.

The best part is that you can even automate entire workflows by linking Actors or connecting them with APIs for more efficient operations. Here’s what you get with Apify for LinkedIn scraping:

Key Features:

• Extract data from LinkedIn profiles, posts, companies, and jobs effortlessly.
• Access global and region-specific data using dynamic IP rotation and geolocation targeting.
• Actors automatically handle CAPTCHA solving, session management, and browser fingerprinting.
• Link actors or use APIs to streamline LinkedIn scraping for large-scale projects.
• Mimic human browsing behavior to bypass detection and boost success rates.

Free Trial: Yes (Based on Actor).

Starting Price: It operates on a Pay-as-you-go pricing structure. The costs vary based on the actor.

Open-Source LinkedIn Scraping Tools

If you prefer flexibility, have your own proxies, and possess coding capabilities, open-source LinkedIn scraping tools might be your go-to option. These tools are ideal for developers or technical teams who want to customize their scraping setup without relying on dedicated scraping APIs.

Based on our detailed research, here are some of the well-known open-source LinkedIn scraping tools:

• linkedin-api by tomquirk: A popular and updated library for LinkedIn scraping. It provides Python-based API calls to fetch profile data, connections, and more.
• LinkedIn-Scraper by TufayelLUS: A straightforward LinkedIn scraper designed to extract public data efficiently with minimal configuration.
• linkedin-profile-scraper-api by josephlimtech: Though slightly outdated, this tool can still be useful for scraping LinkedIn profiles if maintained or updated by users.
• linkScrape by test4a: Another older project, is suitable for those who can adapt its functionality to meet modern LinkedIn scraping needs.

Note: Improper use of open-source scraping tools can easily lead to bans on your LinkedIn account. Always test these tools with dummy accounts and pair them with reliable proxies to avoid detection. Ping Proxies offers the best proxies for scraping, adding a layer of safety to your operations.

LinkedIn Scraper Comparison

Confused about choosing the right LinkedIn scraping tool for your needs? Here’s a quick comparison of different LinkedIn scrapers to help you make a choice.

Step-by-Step LinkedIn Scraper Tutorial

LinkedIn scraping can be done in multiple ways. Whether you’re coding your own script or using ready-made tools, choosing the right approach is important. In this section, I will give you a quick walkthrough of both approaches to help you get started.

Setting Up a LinkedIn Scraper in Python

Creating a LinkedIn scraper in Python can be one of the most efficient ways to gather data. However, this approach comes with significant risks, as LinkedIn actively monitors and blocks such scraping attempts.

Based on research, this method often leads to account bans. It's recommended only for those who can update their scripts by adding security measures which makes them go undetected by LinkedIn's detection mechanisms.

Note: The steps below are designed to give you a general understanding of how scraping works in Python. These scripts and outputs are hypothetical and intended for reference purposes only.

Step 1: Setting Up Your Environment

Start by downloading and installing the latest stable version of Python. Once done, install PyCharm Community Edition or your choice of Python IDE. Now, create a new project in your chosen IDE, name it appropriately, and open a blank script file to begin coding.

Step 2: Installing Required Libraries

Once Python is installed, you'll need to set up essential libraries for web scraping. These include requests for handling HTTP requests and BeautifulSoup4 for parsing HTML Open your terminal or command prompt and execute the following commands:

pip install beautifulsoup4

pip install requests

Note: In case you're proceeding with this approach, tools like Playwright or Selenium are essential for handling dynamic content, login pages, and CAPTCHAs. Adding proxy rotation and CAPTCHA solvers offers better security and reduces the risk of detection.

Step 3: Analyze LinkedIn HTML Structure

You can scrape most data, for demonstration, I will scrape jobs. Start by signing into LinkedIn and navigate to the Jobs section. Search for a job title like “Technical Content Writer” and set the location to “India.” Right-click on a job listing and select Inspect to open the browser’s developer tools.

Now, analyze the HTML structure to identify the tags and attributes containing data such as job titles, company names, and locations.

Step 4: Develop Python Script to Scrape LinkedIn Data

The sample code below fetches the page’s HTML content, parses it using BeautifulSoup, and extracts key details like job titles, employers, locations, and application links.

import requests

from bs4 import BeautifulSoup

url = 'https://www.linkedin.com/jobs/search?keywords=Technical%20Content%20Writer&location=India&pageNum=0'

response = requests.get(url)

if response.status_code == 200:

    soup = BeautifulSoup(response.text, 'html.parser')

    job_listings = soup.find_all('div', {'class': 'job-search-card'})

    for job in job_listings:

        job_title = job.find('h3', {'class': 'base-search-card__title'}).text.strip()

        employer = job.find('a', {'class': 'hidden-nested-link'}).text.strip()

        job_location = job.find('span', {'class': 'job-search-card__location'}).text.strip()

        job_link = job.find('a', {'class': 'base-card__full-link'})['href']

        print(f"[Job Title]: {job_title}")

        print(f"[Employer]: {employer}")

        print(f"[Location]: {job_location}")

        print(f"[Apply Here]: {job_link}")

        print("-" * 40)

else:

    print("Unable to fetch job listings. Please try again.")

Step 5: Run the Python LinkedIn Scraping Script

Upon running the hypothecated script, it fetches job listings directly from LinkedIn. The code processes the page’s HTML to extract details such as job titles, employer names, locations, and application links. Each job listing is displayed in a clean and readable format, making it easy to review.

Using LinkedIn Web Scraper Software

Scraping LinkedIn with scripts can be risky if you don’t know how to create the right one that can handle detection. Web scraper software makes this process easier. These tools manage security and customization for you. All you need to do is choose the right one and start collecting the data you need.

Step 1: Choose your LinkedIn Web Scraper Software

Start by signing up for a tool either by purchasing or opting for a free trial. Once registered, activate your account by completing the basic setup. This step is essential to unlock access to the scraper’s features.

Step 2: Set Up Your Access Details

After activation, you will get unique details such as username, password, and host information. These will allow you to connect to the scraper. Save or note them somewhere secure, as they’re critical for authentication.

Step 3: Configure Your Scraping Environment

Set up the scraper to target LinkedIn pages, like job postings, email addresses, company profiles, etc. Use the tool’s interface or API to customize parameters, such as specific URLs or search criteria.

Step 4: Start Extracting Data

Use the scraper-provided script to pull out details like company descriptions, job listings, employee counts, or your choice of data. The scraper identifies LinkedIn’s structure and fetches the information you need based on your setup.

Step 5: Be Prepared to Face LinkedIn Challenges

To avoid being flagged or blocked by LinkedIn, rely on built-in features like rotating proxies and CAPTCHA-solving tools. Add time delays between requests to make your actions appear more human-like.

Step 6: Verify and Use the Data

Once the scraping is complete, double-check the results for accuracy. Organize the data into usable formats like CSV or JSON. If supported, integrate it directly into your storage or CRM and use the data to your liking.

Note: If you don’t want the hassle of managing the pre-defined scripts, choose the LinkedIn Scraper that offers custom data sets.

Best Practices for LinkedIn Scraping

Based on our research, account bans, and potential legal issues are common for breaking LinkedIn guidelines.

Regardless of the LinkedIn scraping approach, it is important to follow ethical guidelines and respect platform policies. To help you better understand these, I have broken down the key legal considerations and included tips to minimize risks and scrape responsibly.

Legal and Ethical Considerations

The HiQ Labs vs. LinkedIn case highlights why respecting legal and ethical boundaries in LinkedIn scraping is crucial. HiQ scraped member data, created fake accounts, and violated LinkedIn's User Agreement, leading to a permanent injunction.

According to the analysis aggregated by Ping Proxies, such actions not only breach platform policies but also risk legal penalties and damage trust. Now, let’s look at best practices in how you can LinkedIn scraping remains compliant and ethical.

• Follow Robots.txt Guidelines: LinkedIn’s robots.txt file specifies rules for automated site access. Make sure your scraping activities align with these rules to avoid immediate violations.

• Focus on Public Data: Only scrape information that is openly visible without logging into an account. Avoid accessing private or restricted data to stay compliant.
• Review LinkedIn’s Terms: Go through LinkedIn’s terms of service carefully, as they explicitly prohibit scraping activities. Violating these terms can result in penalties or account bans.
• Be Transparent: If you use scraped data for outreach, let individuals know how their information was sourced. Always provide an option to opt out.
• Use LinkedIn APIs: When possible, rely on LinkedIn’s official APIs. They offer a legal and structured way to access data without violating terms.

• Limit Server Requests: Add rate limiting and random delays to your scraping activities to reduce server strain and the likelihood of detection or blocking.
• Use Data Responsibly: Always make sure that the data collected is used ethically. Avoid spamming or misusing information for unauthorized purposes.
• Consult Legal Experts: Laws regarding scraping vary by location. Speak to a legal advisor to confirm your activities comply with local regulations and avoid scraping sensitive or personal data.

Tips to Avoid Being Blocked

LinkedIn uses advanced methods to detect and block automated scraping activities. These include pagination to organize search results, rate limiting to monitor request frequency, CAPTCHA challenges to differentiate bots from humans, and dynamic JavaScript loading that complicates data extraction.

Understanding these mechanisms can help you take the right steps to avoid being flagged or blocked. Here are some practical tips to minimize risks:

• Rotate Proxies Regularly: Use reliable proxies and rotate them frequently to distribute requests across multiple IP addresses, making it harder for LinkedIn to detect unusual activity.
• Implement Random Delays: Add random time intervals between requests to mimic human behavior and avoid triggering LinkedIn’s rate-limiting mechanisms.
• Target Relevant Data Only: Scrape specific data points instead of attempting to extract everything on a page. This reduces the number of requests and minimizes detection risk.
• Use Residential Proxies: Opt for ethically sourced residential proxies with high reputations to simulate requests from real users, lowering the chance of being flagged.
• Simulate Human Browsing: Use browser automation tools to replicate human-like interactions, such as scrolling and clicking, which can reduce suspicion.
• Avoid Overloading Servers: Limit the number of concurrent requests and make sure your script doesn’t overwhelm LinkedIn’s servers with excessive activity.
• Use Stealth Browsers: Tools like headless browsers or advanced scraping frameworks with stealth features can help bypass JavaScript challenges and CAPTCHA triggers.

Conclusion

Scraping LinkedIn data can unlock valuable insights. However, it comes with its challenges, including legal considerations, account bans, and detection risks. By following ethical practices and using the right tools, you can collect the data you need responsibly and efficiently.

If you’re planning to build your own scraper, pairing it with reliable proxies is a must to avoid detection. For a trusted option, check out Ping Proxies. Regardless of the approach, always prioritize compliance with LinkedIn's terms of service, and choose a scraping approach that aligns with your needs and technical expertise.