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LinkedIn Reaction Signals

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Most prospects never post, but they react. Autobound tracks 6 reaction types across 4M+ monitored contacts and enriches every reacted-to post with AI-extracted topics, pain points, and initiatives, so you know what buyers care about before they write a word.

This signal is in early access. Talk to us to join the pilot.

Read the API docs
Reaction types tracked
6
Signal subtypes
8
Update frequency
Daily
Topic categories
50+
Delivery
Signal API, MCP server
Format
JSON

What Are LinkedIn Reaction Signals?

LinkedIn reaction signals capture when a contact reacts (likes, celebrates, and 4 more reaction types) to a post or comment. Most professionals consume far more content than they create, so reactions reveal intent that posting behavior misses: an AE who likes three posts about pipeline acceleration in one week is telling you what's on their mind. Autobound tracks reactions across 4M+ monitored contacts and resolves every person in the signal, including the post author, to a company domain.

Each reacted-to post runs through the same AI pipeline as LinkedIn post signals, so every reaction carries topic tags, a summary, pain points, initiatives, technologies, and competitors extracted from the content the contact engaged with. You know not just that someone reacted, but what business problem the post addressed.

Example subtypes

Topic engagementCategory interestCompetitor contentPain point reactionThought leader engagementIndustry newsProduct announcementHiring surge

From a LinkedIn reaction 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.

  1. 0103

    Most buyers react, few post

    We track reactions from monitored contacts and read the post they reacted to for topics and pain points.

    1. LinkedIn reaction
    LinkedIn reactionJun 5, 2026

    Why pipeline acceleration matters in 2026: deals that closed in 30 days now take 60. The teams winning are feeding real-time signals into their outbound motion...

    Extracted
    HubSpothubspot.comcompanyJordan WilliamscontactLinkedIn reactionsignal_subtype

  2. 0203

    You get one clean record

    Each record names the person, their title and company, labels the event, and links back to the source.

    2. Signal record

    HubSpot

    hubspot.comSoftware Development

    Jordan WilliamsSenior Account Executive

    LinkedIn reactionlinkedinReaction

    B2B growth CEO argues lengthening sales cycles demand signal-driven outbound strategies.

    “Why pipeline acceleration matters in 2026: deals that closed in 30 days now take 60. The teams winning are feeding real-time signals into their outbound motion...”
    Reaction
    Like
    On a post by
    Chris Walker
    Detected
    Jun 5, 2026

    signal_id e7a3c1d9…

  3. 0303

    Act on it where you work

    Send it to Slack, write it to your CRM, or give it to an AI agent.

    When
    A buyer reacts to a competitor's post (post_author_company_domain)
    Do
    Comparison email to that buyer
    3. Your team acts
    or your own code
    #account-signals

    Signal alert from your workflow

    Jordan Williams, HubSpot· LinkedIn reaction

    B2B growth CEO argues lengthening sales cycles demand signal-driven outbound strategies.

    Play: Comparison email to that buyer

    Start the playOpen the source

    Illustration. The record fields are real.

Try it on your own accounts

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

Get a free API keyUse it in Claude via MCP

LinkedIn Reaction Signal Schema

Each reaction signal includes the contact, post context, reaction type, and topic classification.

Full schema docs →
{
  "signal_id": "e7a3c1d9-4f2b-4a8e-9c6d-8b1f3e5a7d2c",
  "signal_type": "linkedin-engagement",
  "signal_subtype": "linkedinReaction",
  "signal_name": "Contact reacted to LinkedIn post",
  "association": "contact",
  "detected_at": "2026-06-05T00:52:59.890Z",
  "contact": {
    "email": "j***@hubspot.com",  // match on email
    "name": "Jordan Williams",
    "first_name": "Jordan",
    "last_name": "Williams",
    "job_title": "Senior Account Executive",
    "linkedin_url": "https://www.linkedin.com/in/jordanwilliams-hs"  // or match on LinkedIn URL
  },
  "company": {
    "name": "HubSpot",
    "domain": "hubspot.com",  // match on domain
    "linkedin_url": "linkedin.com/company/hubspot",
    "industries": ["Software Development"],
    "employee_count_low": 5001,
    "employee_count_high": 10000
  },
  "data": {
    "reaction_type": "LIKE",
    "reaction_target": "post",  // "post" or "comment"
    "is_reshare": false,
    "engagement_date": "2026-06-04T11:37:00.000Z",  // when the reaction happened on LinkedIn; use for recency filtering
    "post_url": "https://www.linkedin.com/feed/update/urn:li:activity:7424978315704250368",
    "post_text": "Why pipeline acceleration matters in 2026: deals that closed in 30 days now take 60. The teams winning are feeding real-time signals into their outbound motion...",
    "post_content_type": "text",
    "post_date": "2026-05-28T14:00:00.000Z",
    "post_author_name": "Chris Walker",
    "post_author_headline": "CEO @ Passetto | B2B Growth Strategy",
    "post_author_linkedin_url": "https://www.linkedin.com/in/chris-walker-b2b",
    "post_author_type": "person",  // "person" or "company"
    "post_author_company_name": "Passetto",
    "post_author_company_linkedin_url": "https://www.linkedin.com/company/passetto",
    "post_author_company_domain": "passetto.com",
    "num_likes": 847,
    "num_comments": 94,
    "num_shares": 31,
    "reaction_breakdown": {
      "like": 612,
      "praise": 147,
      "empathy": 88
    },
    "tags": ["Sales", "Revenue Operations", "Lead Management"],
    "summary": "B2B growth CEO argues lengthening sales cycles demand signal-driven outbound strategies.",
    "pain_points": [
      { "topic": "sales cycles doubling from 30 to 60 days", "intensity": 0.7 }
    ],
    "initiatives": [
      { "topic": "adopting signal-based outbound motion", "urgency": 0.7 }
    ],
    "technologies_mentioned": [],
    "competitors_mentioned": [],
    "source": "person_activity"
  }
}

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 Engagement records, as a CSV.

Talk to sales

See for yourself

Check out sample data: real LinkedIn Reaction 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

How is LinkedIn reaction data collected?
Reaction data is collected from LinkedIn's public-facing activity feeds for monitored contacts and aggregated posts in our topic taxonomy. Data collection is compliant with LinkedIn's terms of service and applicable privacy regulations.
What's the difference between reaction signals and post signals?
Post signals (LinkedIn Posts) indicate that a contact is actively creating content, a higher-effort public commitment. Reaction signals indicate passive interest and topic affinity, broader coverage, lower intensity. Both are valuable; reactions give you 10-20x more signals per contact than posts alone.
Can I filter reactions by topic category?
Yes. The API supports filtering by topic classification, reaction type, post engagement level (minimum reaction count on the post), and recency window. This lets you surface only the high-signal reactions in your category.