LinkedIn is a business-minded social platform with 1 bln. users from over 200 countries. This abundance of professional and industrial data is a focus of interest to recruiters, applicants, marketing specialists, and representatives of companies who want to keep an eye on market trends.
Why You Need to Extract Data from LinkedIn
LinkedIn is a hoard of precious B2B information: millions of professionals of all grades, thousands of companies across all industries, their products, posts about business plans and perspectives, details shared in comments that can be of interest for marketing reasons. That actually explains why LinkedIn is worth crawling:
- Recruitment and job search. Whether you are hiring or applying, you can automate gathering required information. Recruiters can extract CVs, contacts, employment history, and skill sets of the candidates, while applicants can effortlessly scan through job offerings not to miss the best deal. In both cases, web crawling helps make better informed decisions.
- Marketing. Due to crawling, marketing experts head towards the data-driven approach to analyzing consumer behavior, competitor business, and industry trends. This information underlies strategic business decisions and helps companies gain competitive advantage.
- Lead generation. With correct crawling of LinkedIn profiles, posts, and comments, you can find potential customers, partners, and suppliers. While retrieving the data you need, you can develop an efficient outreach strategy.
Methods for Extracting Data from LinkedIn
Today, data retrieval approaches vary from manual copy&paste to automated extraction tools based on Python and neural networks.
In terms of retrieved information, LinkedIn data extraction methods cover user profiles, posts, comments, and likes. You can either use off-the-shelf solutions or develop tailored tools based on different frameworks and libraries.
Since LinkedIn prohibited public access to their API in 2015, you may only access it as a member of LinkedIn’s partner programs that cover cooperation with developers, recruiters, marketing functions, or educators. Becoming a LinkedIn partner is rather a tiresome process, but it’s certainly worth it if you qualify. The point is, LinkedIn doesn’t approve all applications on the spot, and the risk of rejection is high.
This is where alternative solutions come in handy. For example, Google Chrome browser lists a range of LinkedIn scrapers installed as add-ons:
You can choose from other apps:
- Scrapin promises 100% legally compliant, aggregated data on a user or a business with a single click. The solution scans through up-to-date data, returning the relevant information.
- LinkedIn Profile Scraper by PhantomBuster retrieves LinkedIn profile data and supports integrations with Google Sheets and HubSpot CRM, thus delivering new contacts to users and underlying the lead generation strategy.
- Datagma is an efficient tool designed for recruiters. In addition to providing user information, it also cleans the retrieved data, eliminates incorrect positive results, and verifies contact details.
Besides, a group of tools deliver access to LinkedIn data via API:
- The REST-based Lix LinkedIn API returns data on LinkedIn profiles, jobs, search, posts, connections, and contacts; the solution manages accounts, proxy rotation, and rate limits.
- LinkedIn Scraper API supports bulk request handling, data parsing, and validation. It also features IP and user agent rotation, JavaScript rendering, and bypassing CAPTCHAs.
- Proxycurl’s Search API offers search through companies or users at LinkedIn or full, legally compliant, up-to-date datasets of user profiles in JSON format.
You can also find crawling solutions based on modern technologies, such as AI or Python libraries and frameworks.
Using a Proxy to Parse Data from LinkedIn
Users who plan to collect LinkedIn data should be aware that user agreements on this social network do not allow scraping. So, unless you are a member of the four official LinkedIn programs, such data retrieval can be treated as a violation and lead to different penalties, such as account ban or litigation.
In this context, proxies start playing an even greater role than usual. Since proxies substitute your IP address and mask your identity, they help you log into LinkedIn anonymously, bypass LinkedIn authwall and lower the chances of bans.
Besides, they ensure higher speed and better geo targeting. With multiple proxies and IP rotation, you can send more requests in less time, thus accelerating and scaling up scraping. And if you choose proxies from a certain location, you will reach the data from a specific region you wouldn’t have been able to access with your original IP.
So, before starting any LinkedIn scraping, decide, whether you need static or rotating datacenter or residential proxies, choose a reliable proxy supplier, and go ahead being fully aware that proxies are one of the most important tools to bypass sign-in authwall in LinkedIn.
Examples of Step-by-Step Instructions
In Python, you can develop a fully customized solution. Install Python 3, Requests, and Beautiful Soup.
- Create a new file and import the required libraries:
- Open <Chrome DevTools> to explore the LinkedIn website structure. In LInkedIn, data on every job is put in a card-like container that is wrapped between a <li> tag and an <ul> element. Besides, LinkedIn employs infinite scrolling pagination without the <next page> button, so you need Selenium to retrieve this data.
- To parse LinkedIn, create a variable with the initial URL and pass it to the <requests.get()> method. Make sure the returned HTML is stored in a variable called <response> to create the Python object.
- To extract the data, parse the raw HTML data to ensure simpler navigation with CSS selectors. For this, pass the <response.content> as the first argument and the parser method as the second argument. It will create a Beautiful Soup object:
- Define a new function with the whole code to pass webpage and <page_number> as arguments. These arguments can be used to create the URL sending the HTTP request.
- The following If-condition is required to increase the start parameter in a loop:
- To retrieve the data, create a new variable that will choose div’s with the class a user can focus on:
- After retrieving a list of <div>s, use the chosen CSS selectors to create <for loop>:
- To import the extracted data to the CSV file, first open a new file, create a writer, and make it create the heading row with the <.writerow()> method:
- To create a new row with the retrieved data, add this snippet at the end of the <for loop>:
- To ensure the file is closed, if the loop breaks, add an else statement:
- To bypass LinkedIn anti-bot measures, use some dedicated tools, for example, ScraperAPI. First, create the account in this app, then add the following string at the beginning of URL:
It will create this function call that ensures IP rotation:
Artificial intelligence. Advocates of zero-code tools can opt for AI-based solutions, for example, Bardeen. It extracts data of LinkedIn profiles, and posts,, and can additionally create AI-generated recruiting emails. Besides, it supports integrations with Google Sheets, Airtable, Notion, Coda, Pipedrive, and ClickUp.
If you want to extract data from LinkedIn profiles, follow these steps:
- Install and open the Bardeen Chrome extension;
- Search the automation called <copy LinkedIn profile data to Google Sheets>. In the builder, you can configure the automation the way you want;
- In the <Argument> section of the chosen template, add the Google Spreadsheet to copy extracted data to;
- Save the automation and close the builder;
- Open the LinkedIn profile, then click on the automation, and the profile details are copied to the spreadsheet you specified;
- Repeat these actions for any number of profiles;
- To further automate this task, you can create a right-click automation. Open your automation in the builder, add the trigger called <right-click>, invent a name for this automation. Click <Done>, <Save< and <Close>;
- Open the LinkedIn user profile, right-click on a screen, click on the name you gave to the automation. The data will be automatically copied to your spreadsheet.
Summary
As you can see, there are various alternatives that allow data extraction from LinkedIn. Most importantly, respect LinkedIn rules and restrictions, and choose the solution that suits your needs and skill set.
Frequently Asked Questions
Please read our Documentation if you have questions that are not listed below.
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Is LinkedIn scraping legal?
The Terms of Service of LinkedIn clearly prohibit any automated data collection, whether by means of scraping, or any other technique. Violating this rule can lead to ethical issues and legal consequences. So weigh all the pros and cons before you commence any LinkedIn scraping.
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What are the steps to access LinkedIn APIs?
Examine LinkedIn's Terms of Use; Apply to access LinkedIn's APIs in your business case is qualified; Develop an app in compliance with LinkedIn's API terms; Ask users to grant you permission through the OAuth 2.0 authorization process; Do not exceed the established rate limit; Ensure protection of user’s personal data.
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Is there any official permitted way of LinkedIn scraping?
Yes. LinkedIn has several APIs authorized developers and companies can access to retrieve some LinkedIn information for legal purposes, for example, for integrations and app development.
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At first sight, users, who need to scrape dynamic content data, may face some difficulties. However, modern developer tools help resolve virtually any issue. So, if you think of setting up proxy rotation to crawl dynamic content, Python and Selenium are likely to be among your best friends.