Social Intelligence

LinkedIn Reaction Signals

Most prospects never post, but they react. Autobound tracks 7 reaction types across 4M+ 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.

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Reaction types tracked

Signal subtypes

Daily

Update frequency

50+

Topic categories

Social Intelligence8 subtypes · Daily refresh

What Are LinkedIn Reaction Signals?

LinkedIn reaction signals capture when a contact reacts (likes, celebrates, and 5 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 Signal Subtypes

topicEngagementcategoryInterestcompetitorContentpainPointReactionthoughtLeaderEngagementindustryNewsproductAnnouncementhiringSurge

Data Schema

LinkedIn Reaction Signal Schema

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

linkedin_reactions.schema.json

{
  "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.williams@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"
  }
}

GCS Bucket: gs://autobound-linkedin-reactions/
Formats: JSONL · Parquet
Refresh: Daily

Full schema docs →Measured coverage →

Applications

What teams build with LinkedIn Reaction Signals

03 documented applications

01
Interest-Based Personalization
When you know a prospect has been reacting to content about pipeline efficiency, your cold email doesn't have to be cold. Open with the theme, not the behavior, 'Sounds like pipeline velocity is top of mind for a lot of AEs right now' lands very differently than a generic intro.
02
Account Prioritization
Score accounts based on reaction signal density. An account where 3+ contacts are reacting to content in your category over a 30-day window is showing buying committee engagement, much higher priority than an account where only one person opened your email.
03
Timing-Based Outreach
React to the same post your prospect just reacted to, then send a LinkedIn DM or email within 24-48 hours. You share a piece of content that resonated with both of you, it's a genuine, low-friction opening.

Worked Example

One signal, traced to outcome

One LinkedIn Reaction Signals record followed from detection, through the action a rep took on it, to the measured result. Illustrative composite of observed customer workflows.

01 signal → 02 action → 03 outcome

Signal detected

Jordan Williams at HubSpot reacts to 6 pipeline-related posts over 30 days, including a high-traction post on sales cycle compression.

Action taken

Your AE spots the pattern, sends a pipeline-themed email referencing the broader trend (not the specific posts), and asks for 15 minutes.

Outcome

Jordan replies the same day, 'this is literally what we're working on right now.' Demo booked.

Run this against live data →

FAQ

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.

How It Works

From Raw Data to Your Stack

Autobound ingests from LinkedIn API, Glassdoor, GitHub, Reddit, G2, extracts structured signals with AI, and delivers them however your infrastructure needs.

Autobound Ingests

Raw data from LinkedIn API, Glassdoor, GitHub, Reddit, G2 is continuously collected and normalized across millions of sources.

AI Extracts & Scores

ML models extract 8 signal subtypes with relevance scoring, confidence levels, and entity resolution.

You Receive

Structured JSONL delivered via your preferred method, updated on a daily cadence.

REST API

Real-time access with subtype filtering

300 req/min

GCS Push

Automated delivery to your bucket

JSONL + Parquet

Enrich API

On-demand LLM-ranked insights

AI relevance scoring

Flat File

Bulk exports for data warehouses

CSV, JSON, Parquet

3 vendors consolidated

“By consolidating three data vendors into Autobound's Enrich API, we added 100+ new signal types and saved 4 months of engineering time.”

AiSDR Team

Engineering, AiSDR

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