How Can You Scrape LinkedIn Comments?
Anyone who liked or commented on a competitor's post has already raised their hand. This walkthrough skips the theory and goes straight to the tool, the exact clicks, and the warm lead list waiting on the other side.
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Pick a founder whose audience already overlaps with yours
Choose someone building a competing or complementary product on LinkedIn, ideally someone whose followers look like your own future customers. If you were building something like LinkedIn itself, a natural pick would be scraping everyone who engaged with Reid Hoffman's posts. Copy that person's profile URL before moving on.
Load the LinkedIn Post Commenter & Liker Scraper
Inside Phantombuster, open the LinkedIn Post Commenter & Liker Scraper and click "Use now" to load it. When it asks for a source, choose "LinkedIn Profile Page," then paste in the profile URL you copied. Hit Save, then Save again to finish setup.
Flip the Phantom from OFF to ON
On the Phantom's dashboard, find the switch labeled OFF and click it to start the scrape. Once it is running, Phantombuster works in the background, so you do not need to keep the tab open or babysit it.
Give it real time before checking results
Scraping LinkedIn comments and likes is not instant. Give the Phantom at least 15 minutes before checking in, and plan for an hour or more before your list is genuinely ready to use. Checking too early just shows an empty or half-finished report.
Where your scraped leads actually show up
Open the "Report" tab on the Phantom's dashboard to watch commenters and likers land in your list as the scrape runs. That report feeds the next step: turning these names into a CSV you can actually work from.
What this scraper actually captures
"Scrape LinkedIn comments" sounds broad, but this specific Phantom only grabs people who engaged with one profile's posts, meaning everyone who liked or commented gets pulled into a list. It does not touch private messages, connections, or anything outside public post activity. The output is a name plus profile link for every commenter and liker, ready to filter and reach out to. Inside the interactive mission, each of those fields gets flagged the moment Phantombuster asks for it, so you are never staring at the dashboard wondering which box to fill in next.
Why comment and like scraping beats a cold LinkedIn list
A generic LinkedIn scraper pulls people based on job title or company size, a guess about who might care. Scraping commenters and likers on a specific post instead targets people who have already shown interest in that exact problem space, a warmer signal than almost any other free targeting method. Once your report is full, the natural next step is exporting those leads to a CSV and working them through an outreach sequence, which is exactly what the rest of the B2B mission covers.
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Frequently asked questions
How can you scrape linkedin comments and likes from a single post?
Use a LinkedIn-specific scraping tool like Phantombuster's Post Commenter & Liker Scraper. Point it at a profile, choose "LinkedIn Profile Page" as the source, save your setup, and turn the Phantom on. It runs in the background and returns a list of everyone who liked or commented on that person's recent posts.
Is it legal to scrape LinkedIn comments and profiles?
Scraping publicly visible information, like who liked or commented on a post, sits in a legal gray area that depends on your location, LinkedIn's terms of service, and how you use the data afterward. LinkedIn's terms restrict automated scraping, so there is real platform risk, like account restrictions, even when the data itself is public. Treat this as a growth tactic with tradeoffs, not a risk-free default.
What is the difference between a general LinkedIn scraper and a post commenter and liker scraper?
A general LinkedIn profile scraper or LinkedIn data scraper pulls information based on search filters like job title, company, or location. A post commenter and liker scraper instead targets people who already engaged with one specific post, which usually means warmer, more relevant leads than a broad list built on filters alone.
Do you need LinkedIn Sales Navigator to scrape LinkedIn profiles this way?
No. This method only needs a public LinkedIn profile URL as the source, not a Sales Navigator seat. Sales Navigator scrapers exist for search-based list building, but comment and like scraping works off any public post, free account included.
How long does it take to scrape LinkedIn comments from a post?
Plan for at least 15 minutes before checking results, and closer to an hour or more before the list is fully populated. The exact time depends on how many people engaged with the source post.
Is Phantombuster free to use for scraping LinkedIn?
Phantombuster offers a free trial that is enough to run this scrape and see the results firsthand. Ongoing, repeated scraping at volume eventually calls for a paid plan, but you do not need one to test this out.
What do you actually get after scraping a LinkedIn post's commenters and likers?
A report inside Phantombuster listing each person who liked or commented, along with their profile link. From there, the natural next step is exporting that list to a CSV so you can start reaching out.
Choose a source profile with real audience overlap
The people commenting on a founder's posts only make good leads if that founder's audience actually resembles your buyer. A wildly popular but unrelated account still produces a long list, just not a useful one.
Let the scrape actually finish
Checking in under 15 minutes almost always shows an empty or partial report. Give the Phantom real time to run before deciding whether the source profile was a good pick.
Treat the list as warm, not qualified
Liking or commenting on a post shows interest in the topic, not necessarily fit for your product. Skim the list before you reach out at scale, rather than messaging every name it returns.
Rotate your source profile periodically
Scraping the same founder's posts repeatedly returns a lot of the same people. Refresh your source every so often so your lead list keeps finding new people, not the same warm audience twice.
Why people drop off before they finish
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