Reactions vs comments vs shares: which LinkedIn signal shows the most intent?
Depth of engagement tracks intent: a thoughtful comment or a share costs more effort, and often reveals the person's own view of the problem, than a passive reaction, so weight comments and shares higher, then layer ICP fit. No public study ranks these precisely, which is exactly why first-party data on it is so valuable.
Last reviewed: July 2026
The engagement ladder
Not all engagement is equal. Think of it as a ladder of effort: a passive reaction (a like) sits at the bottom, a thoughtful comment in the middle, and a share, especially one with the person's own commentary, near the top. The more effort an action takes, the more it tends to reveal genuine interest.
Why comments and shares beat reactions
A reaction is one click. A comment requires forming and typing a thought, and it often exposes the person's own view of the problem, which is gold for a relevant first touch. A share puts their name behind the idea to their own network. Both are stronger signals of engagement with the topic than a like.
The exceptions: fit still rules
Depth is a tie-breaker, not the whole story. A substantive comment from someone who is not your buyer is still noise. The right model is effort multiplied by fit: weight the engagement by how much effort it took, then multiply by how well the person matches your ICP. Acting on real intent signals pays, deals that included them ran about 2x larger in one benchmark (Dreamdata and G2, 2024), but only when the person is actually a fit.
How to score engagement fast
A simple rubric works: give a share the most weight, a comment slightly less, a reaction the least, then multiply by an ICP-fit score. Prioritize the top of that list, especially since only about 5% of your market is in-market at any time (Ehrenberg-Bass), so the strongest early signals are worth acting on first.
An honest caveat
There is no public study that precisely ranks how comments, reactions, and shares convert. The effort-based logic above is sound and widely accepted, but the exact multipliers are unproven in published research. That gap is precisely why first-party data here is valuable: a tool watching thousands of engagements can measure the real conversion difference. (We will publish our own aggregate, fully anonymized version of this as our dataset grows.)
Slingapult's read: weight engagement by effort, then by fit. It is the cheapest way to sort a public audience into who is worth a warm, human first touch.