A LinkedIn post's reaction list is close to a ready-made lead list: everyone on it already engaged with a topic you can speak to. The question is which of the several ways to pull that list out actually gets you a usable spreadsheet, and at what cost.
There are six real ways to do it, and they differ far more in cost, setup, and what you walk away with than most roundups let on. Here is the comparison built around that.
The six methods, compared
| Method | Setup required | Runs from | Cost | Exports |
|---|---|---|---|---|
| Read the reactions list by hand | None | Your browser | Free | No |
| Public engagement finder | None | A server, on public data | Free | Yes |
| Data marketplace or actor | Sign up with the provider | A third party | Paid | Yes |
| Browser extension | Install it | Your browser | Paid | Yes |
| Always-on cloud tool | One-time setup | Their servers | Paid | Yes |
| Official LinkedIn API | Not applicable | Not available | Not available |
Two rows deserve expanding, because they are the ones people get wrong.
The always-on cloud tool row is the fastest to set up and the most expensive. Set it up once, and it keeps pulling likers on its own schedule from then on, so you never have to go back and export the list again. That ongoing convenience, not the data, is what the subscription price buys. PhantomBuster is one such tool, and the tools that replace PhantomBuster for likers are compared on their own page.
The official API row is a genuine dead end, and it is worth saying plainly rather than implying otherwise. LinkedIn's partner APIs do not expose the reaction list of an arbitrary post to third-party developers. If a page tells you to "just use the official API" for this, it has not tried.
The manual method, and where it stops
Open the post, click the reaction count, scroll the panel. This is free, it breaks no rules, and for a post with thirty reactions it is genuinely the right answer.
It stops being the right answer quickly. The panel loads in small batches, there is no export, and there is no way to filter. By a few hundred reactions you are scrolling a modal and retyping names into a spreadsheet, and every name a hand mistypes is a prospect the search bar can never find again.
Paste a post.
Get the people who liked it.
The likes finder reads a public post and hands back the people who reacted, in a list you keep. Free to try.
Find who liked a post
Using the public engagement finder
On a public post, anyone can see how many people reacted, but LinkedIn shows the list of who reacted only to signed-in members.
- Copy the post URL. Use the full permalink, not the shortened share link. Open the post on its own page and copy the address bar.
- Paste it into the finder. The LinkedIn likes finder takes the URL and returns the people.
- Filter before you export, not after. This is the step everyone skips. Cutting the list down while you still have it on screen is faster than cleaning a CSV later.
Time: a couple of minutes. Setup: none.
The same applies to commenters, which are a stronger signal and usually a shorter list. The LinkedIn comments finder works the same way.
Qualifying the list is the half that actually matters
A list of 400 names is not 400 leads, and the export is the easy half. What you do next is what separates a useful hour from a wasted one.
Reactions are a weak signal on their own. A like costs nothing and often means "I read the first line". Comments cost effort, so they carry more intent. Neither tells you the person has a problem you solve. The qualification step is what converts an engagement list into a prospect list, and skipping it is why most engagement exports go stale in a spreadsheet.
Two filters do most of the work:
- Fit. Does this person match who you actually sell to? Title alone is a poor proxy. Company shape usually matters more.
- Relevance of the post. Someone who engaged with a post about the problem you solve is worth far more than someone who engaged with a viral post about hiring. The topic of the post is part of the signal, not just the act of engaging.
The reason this matters shows up in reply rates. Expandi's 2026 outreach benchmarks, built on more than 13 million connection requests and 6.7 million messages sent between May 2025 and April 2026, put the reply rate on outbound messages at 10.4%, more than three times the 3.0% reply rate on a cold connection request. That gap is real, but it is not magic, and it collapses the moment "warm" only means "this person clicked something once".
For the longer version of how to read engagement as intent, see signal-based selling on LinkedIn.
What to do with the list once it is qualified
Referencing the specific post is the whole advantage, and it is easy to waste. "I saw you liked a post about X" is not personalisation, it is surveillance with extra steps. The better version references the idea, not the click: you are writing to someone because they are engaged with a topic, and you have something useful to say about that topic.
If you are sending from your own account, pace it. The list arriving all at once does not mean the outreach should.
Frequently asked questions
Does a post's audience setting affect whether you can export its likers?
Yes. A post shared publicly has a publicly readable reaction list, so any of the methods above can retrieve it. A post shared to a restricted audience (connections only, or a smaller group) is not publicly readable and will not appear in any export.
Is there an official LinkedIn API for post reactions?
No. LinkedIn's partner APIs do not expose the reaction list of arbitrary posts to third-party developers. Any guide telling you to use the official API for this is describing something that is not available.
Are likers or commenters the better lead source?
Commenters, in almost every case. Commenting costs visible effort and usually says something about the person's view, which gives you both a stronger intent signal and a better opening. Liker lists are longer and weaker.
How many people can you export from one post?
The practical limit is the post itself rather than the method. Most posts have far fewer engagers than people expect, and the useful subset after qualification is smaller again. Optimising for list size is usually the wrong goal.
