Social Intelligence

LinkedIn Comment Signals

Published February 6, 2026Updated October 1, 2026

High-intent signals from prospect LinkedIn comments. AI-filtered for signal quality with pain point extraction, initiative detection, technology mentions, and relationship context across 4M+ monitored contacts.

Read the API docs

Contacts Monitored: 4M+
Signal Subtypes: 1
Refresh Cadence: Monthly
Signal Yield: ~15-20% of comments

Delivery: REST API, GCS Push, Generate Insights API, Flat File
Format: JSONL or Parquet
Bucket: gs://autobound-linkedin-comments-contact-v2/

What Are LinkedIn Comment Signals?

LinkedIn comment signals are high-intent signals extracted from prospects' comments on other people's posts, which often reveal more candid thinking than curated personal posts. Autobound filters comment activity aggressively for quality: short comments, celebratory reactions, and colleague interactions are removed (96% noise reduction), leaving roughly 15-20 high-value signals per 100 raw comments.

Each signal includes the full comment text, parent post context, AI-classified intent (question, disagreement, insight, recommendation), pain points with intensity scoring, initiatives with urgency scoring, and inferred relationship to the poster. Referencing someone's thoughtful comment on an industry post is one of the most natural conversation starters in B2B sales, far more effective than citing a funding round or headcount growth.

Example record

LinkedIn commentlinkedinPostComment

Example from the signal catalog

Maya Chen, CTO at Halyard Systems, commented on a post about zero-trust adoption, asking about vendor consolidation.

From a LinkedIn comment to your next move

What we detect, what you get, and what you do with it. Pick the path that fits your team.

Sales, marketing and RevOps: Slack, CRM fields and plays.

0103
Comments are more candid than posts
We read prospects' comments on other people's posts, drop the noise, and keep the ones that show a pain or a plan.
1. LinkedIn comment
2. Signal record
3. Your team acts
1. We detectLinkedIn comment
2. You getSignal record
3. You actSlack, CRM or agent
LinkedIn commentJun 23, 2026
Observability for ML inference pipelines is still five years behind where we are with traditional services. We're investing heavily in building that layer internally because nothing on the market handles the throughput and cost attribution we need at scale.
Extracted
Databricksdatabricks.comcompanyRyan KovacscontactLinkedIn commentsignal_subtype
0203
You get one clean record
Each record names the person, their title and company, labels the event, and links back to the source.
1. LinkedIn comment
2. Signal record
3. Your team acts
1. We detectLinkedIn comment
2. You getSignal record
3. You actSlack, CRM or agent
Databricks
databricks.comSoftware Development
Signal recordf40afdf7…
Ryan KovacsVP of Data Engineering
LinkedIn commentlinkedinPostComment
Commenter argues ML inference observability lags traditional services; team is building the layer in-house
“Observability for ML inference pipelines is still five years behind where we are with traditional services. We're investing heavily in building that layer internally because nothing on the market handles the throughput and cost attribution we need at scale.”
Intent
Insight
On a post by
Kevin Tran
Detected
Jun 23, 2026
signal_id f40afdf7…
0303
Act on it where you work
Send it to Slack, write it to your CRM, or give it to an AI agent.
When
comment_intent shows evaluation on a competitor's post
Do
Social DM plus an email that offers a comparison
1. LinkedIn comment
2. Signal record
3. Your team acts
1. We detectLinkedIn comment
2. You getSignal record
3. You actSlack, CRM or agent
or your own code
#account-signals
Signal alert from your workflow
Ryan Kovacs, Databricks· LinkedIn comment
Commenter argues ML inference observability lags traditional services; team is building the layer in-house
Play: Social DM plus an email that offers a comparison
Start the playOpen the source
Illustration. The record fields are real.

0103
Comments are more candid than posts
We read prospects' comments on other people's posts, drop the noise, and keep the ones that show a pain or a plan.
0203
It lands in your stack
Land monthly JSONL from your GCS bucket. Join on contact.linkedin_url.
0303
Ship it in your product
Feed comment_text and pain_points into an engagement score.

LinkedIn comment
Signal record
Your team acts

We detectLinkedIn comment
You getSignal record
You actSlack, CRM or agent

LinkedIn commentJun 23, 2026

Observability for ML inference pipelines is still five years behind where we are with traditional services. We're investing heavily in building that layer internally because nothing on the market handles the throughput and cost attribution we need at scale.

Extracted

Databricksdatabricks.comcompanyRyan KovacscontactLinkedIn commentsignal_subtype

Try it on your own accounts

1,000 free credits. Query linkedin-comment in the Signal API or from Claude.

Get a free API key Use it in Claude via MCP

How we build this signal

How we build each LinkedIn comment signal

We start from the posts in our LinkedIn post feeds and keep the substantive comments people leave on them.

Min. comment length: 50 characters
Comments kept per post: Top 5

01
Collection
For each post in our LinkedIn post feeds we read up to 10 of its comments. A post already handled in the run is not read twice.
02
Comment filter
Only comments by a person, not a company page, and at least 50 characters long are kept. Of those, the 5 with the most likes and replies per post move on.
03
Commenter match
Each commenter is matched by LinkedIn URL to our contact records, which add their name, title and email, and their company is joined from our company records.
04
Extraction and quality filter
A model reads each comment for pain points, initiatives and technologies, and labels its intent. A comment ships only if it names one, asks a question, disagrees, or scores 0.5 or higher on signal quality. Congratulations, praise and tag-a-friend comments are dropped.
05
Delivery
Each record carries the comment, the post it answers and the commenter. It is keyed on commenter and comment URL, so a shipped comment is never delivered again.

LinkedIn Comment Signal Schema

Comment signals include AI-filtered quality scoring, intent classification, pain point and initiative extraction, parent post context, and relationship inference.

Full schema docs →

{
  "signal_id": "f40afdf7-bd1f-4362-8dee-78b75aced2b7",
  "signal_type": "linkedin-comment",
  "signal_subtype": "linkedinPostComment",
  "detected_at": "2026-06-23T13:56:54.841Z",
  "association": "contact",
  "contact": {
    "first_name": "Ryan",
    "last_name": "Kovacs",
    "full_name": "Ryan Kovacs",
    "email": "r***@databricks.com",  // match on email
    "job_title": "VP of Data Engineering",
    "linkedin_url": "https://www.linkedin.com/in/ryankovacs"  // or match on LinkedIn URL
  },
  "company": {
    "name": "Databricks",
    "domain": "databricks.com",  // match on domain
    "linkedin_url": "https://www.linkedin.com/company/databricks",
    "industries": ["Software Development"],
    "employee_count_low": 5001,
    "employee_count_high": 10000
  },
  "data": {
    "comment_summary": "Commenter argues ML inference observability lags traditional services; team is building the layer in-house.",
    "comment_text": "Observability for ML inference pipelines is still five years behind where we are with traditional services. We're investing heavily in building that layer internally because nothing on the market handles the throughput and cost attribution we need at scale.",
    "comment_url": "https://www.linkedin.com/feed/update/urn:li:activity:7412575850081996801",
    "comment_num_likes": 18,
    "comment_num_comments": 3,
    "comment_intent": "insight",  // question, disagreement, insight, recommendation, ...
    "signal_quality": 0.9,
    "relationship_context": {
      "inferred_relationship": "industry_peer",
      "confidence": 0.7
    },
    "pain_points": [
      { "topic": "ML inference observability gaps at scale", "intensity": 0.8 }
    ],
    "initiatives": [
      { "topic": "building in-house inference monitoring layer", "urgency": 0.8 }
    ],
    "technologies_mentioned": [],
    "parent_post": {
      "post_summary": "Head of AI infrastructure discusses observability gaps in production ML systems.",
      "num_likes": 212,
      "num_comments": 34,
      "poster_name": "Kevin Tran",
      "poster_job_title": "Head of AI Infrastructure"
    }
  }
}

Bulk data

Need this signal for 10,000+ companies?

Get a free sample file or a bulk feed (API, GCS push, flat file). The sample is real LinkedIn Comments records, as a CSV.

Talk to sales

Measured coverage

What's inside LinkedIn Comment Signals

A dated snapshot counted in our production database, not a live count. Minimums, not ceilings.

Coverage for all 35 signal types →

138,146

Contacts with fresh data per month

6-month average, records through Jul 21, 2026

226,627

Records created per month

Published baseline, records through Jul 21, 2026

Top subtypes

1 total

Linkedin Post Comment1.6M · 100%

Seniority

full breakdown →

C-Suite97K · 13%

Staff87K · 12%

Manager39K · 5.3%

Director34K · 4.7%

VP33K · 4.6%

Consultant3.4K · 0.5%

Departments

full breakdown →

Business Management102K · 14%

Sales21K · 2.9%

Technology, Software, Information Technology19K · 2.6%

Executive16K · 2.2%

Marketing16K · 2.2%

Operations11K · 1.5%

Education9.6K · 1.3%

Join keys

fill rate

Contact LinkedIn100%

Contact name64%

Email28%

Records measured 1,586,395+Contacts reached 729,948+

See for yourself

Check out sample data: real LinkedIn Comment records with the same fields the API returns.

Read the API docs

“Autobound's API streamlined our data management, accelerated product development, and improved outreach quality.”

AiSDR Team, Engineering Team, AiSDR

Frequently asked questions

What are LinkedIn comment signals?

LinkedIn comment signals capture high-intent interactions where contacts comment on posts relevant to your product category. Autobound filters for substantive comments, not simple reactions, and uses AI to extract pain points with intensity scoring, initiative mentions, and relationship context. Coverage spans 4M+ monitored contacts with monthly refresh cadence.

How does Autobound detect LinkedIn comment signals?

Autobound monitors LinkedIn activity for tracked contacts on a monthly cadence. When a contact posts a substantive comment, AI models analyze the text for pain points, initiative mentions, technology references, and competitive signals. Quality filtering removes low-value interactions like congratulatory replies.

How should I use LinkedIn comment data in my outreach?

Reference the specific comment in your outreach, like 'Your comment about X really resonated,' and immediately connect it to your value proposition. Comments are the highest-intent social signal because they represent a voluntary, public expression of opinion. Keep your outreach conversational and solution-oriented rather than salesy, matching the tone of the original comment thread.

LinkedIn Comment Signals

What Are LinkedIn Comment Signals?

Example record

From a LinkedIn comment to your next move

Comments are more candid than posts

You get one clean record

Act on it where you work

Comments are more candid than posts

It lands in your stack

Ship it in your product

How we build each LinkedIn comment signal

Collection

Comment filter

Commenter match

Extraction and quality filter

Delivery