Social Intelligence

Twitter/X Post Signals

What individual prospects say on Twitter/X, resolved to business contacts and analyzed by AI. Every tweet ships with engagement metrics (likes, reposts, views), account metadata, and extracted pain points, initiatives, technologies, and competitors across 4M+ contacts.

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4M+

Companies Tracked

Signal Subtypes

Weekly

Refresh Cadence

~300 categories

Tag Taxonomy

Social Intelligence1 subtypes · Weekly refresh

What Are Twitter/X Post Signals?

Twitter/X post signals capture what individual prospects say on Twitter/X: opinions, challenges, tool complaints, and industry takes. Autobound matches Twitter handles to contacts through LinkedIn profile links and name matching, then analyzes each tweet with AI to extract topic tags from a 300+ category taxonomy, a 10-15 word summary, pain points with intensity scores, initiatives with urgency scores, technologies mentioned with adoption status, and competitors referenced.

Each signal includes full engagement data (likes, reposts, replies, views, bookmarks, quotes) plus account context: bio, follower count, location, and verification status. Twitter/X is less polished than LinkedIn, so candid pain points and tool evaluations often surface here first. Coverage spans 4M+ contacts, refreshed monthly.

Example Signal Subtypes

twitter_post

Data Schema

Twitter/X Post Signal Schema

Twitter/X signals include full tweet text, LLM-generated topic tags, engagement metrics with view counts, and verified account metadata.

twitter_x_posts.schema.json

{
  "signal_id": "dbc467e8-84b3-4763-972a-deb88e75112a",
  "signal_type": "social_media",
  "signal_subtype": "twitter_post",
  "signal_name": "Twitter Post",
  "association": "contact",
  "detected_at": "2026-06-13T16: 31: 43.993Z",
  "contact": {
    "name": "Marcus Webb",
    "job_title": "VP of Engineering"
  },
  "company": {
    "name": "Loop Returns",
    "domain": "loopreturns.com",
    "description": "Returns management platform for ecommerce brands",
    "industries": ["Software Development"]
  },
  "data": {
    "post_url": "https://x.com/marcuswebb_dev/status/2061702360843788335",
    "post_text": "Six months into our Kubernetes migration and the observability bill is now bigger than the compute bill. Something is deeply wrong with how this industry prices monitoring.",
    "posted_date": "Thu Jun 11 15: 05: 23 +0000 2026",
    "tweet_id": "2061702360843788335",
    "num_likes": 412,
    "num_reposts": 58,
    "num_replies": 63,
    "num_views": 48210,
    "num_bookmarks": 91,
    "num_quotes": 12,
    "is_reply": false,
    "replied_to_username": null,
    "posting_source": "Twitter Web App",
    "language": "en",
    "hashtags": [],
    "urls": [],
    "mentions": [],
    "contact_twitter_url": "https://x.com/marcuswebb_dev",
    "contact_twitter_handle": "marcuswebb_dev",
    "contact_twitter_bio": "VP Eng @ Loop Returns. Infra, on-call, and opinions.",
    "contact_twitter_followers": 8214,
    "contact_twitter_following": 903,
    "contact_twitter_location": "Columbus, OH",
    "contact_twitter_verified": true,
    "contact_twitter_verification_type": "blue",
    "contact_twitter_account_created": "Tue Aug 11 14: 52: 44 +0000 2015",
    "contact_twitter_total_tweets": 11406,
    "contact_twitter_dm_open": true,
    "tags": ["Infrastructure", "DevOps", "Spending/Investment"],
    "summary": "VP of Engineering says observability costs now exceed compute spend after Kubernetes migration.",
    "pain_points": [
      { "topic": "observability costs exceeding compute spend", "intensity": 0.8 }
    ],
    "initiatives": [
      { "topic": "completing Kubernetes migration", "urgency": 0.7 }
    ],
    "technologies_mentioned": [
      { "name": "Kubernetes", "status": "migrating_to" }
    ],
    "competitors_mentioned": []
  }
}

GCS Bucket: gs://autobound-twitter-company-v1/
Formats: JSONL · Parquet
Refresh: Weekly

Full schema docs →Measured coverage →

Applications

What teams build with Twitter/X Post Signals

04 documented applications

01
Social Selling with Real-Time Hooks
Reference a company's recent tweet in your outreach for an immediately relevant conversation starter. Timeliness is key. Twitter/X posts have a shorter attention window than LinkedIn.
02
Competitive Intelligence Monitoring
Track when competitors and target accounts tweet about product launches, pricing changes, or partnerships. Twitter/X is often the first channel for breaking announcements.
03
Brand Engagement Analysis
Follower counts, engagement rates, and verification status indicate how seriously a company takes its social presence, and whether social selling channels will reach decision-makers.
04
Event and Campaign Timing
When companies tweet about events, conferences, or seasonal campaigns, those are natural moments to align your outreach with their marketing calendar.

Measured, Not Estimated

What's actually inside Twitter/X Post Signals

Volumes, subtypes, geography, and join-key fill rates audited directly from our production database, so you can verify coverage before you buy.

Measured coverageaudited from production · minimums, not ceilings

172,607

contacts with fresh data / month

basis: 30-day window

1,343,094

records created / month

basis: 30-day window

Top subtypes

1 total

Twitter_post2.8M · 100%

Seniority

full breakdown →

Staff99K · 44%

C-Suite47K · 21%

Manager26K · 11%

Director16K · 7.0%

VP13K · 5.7%

Consultant2.1K · 0.9%

Departments

full breakdown →

Business Management50K · 22%

Technology, Software, Information Technology17K · 7.4%

Education12K · 5.3%

Sales10K · 4.5%

Creative, Design8.1K · 3.6%

Marketing7.5K · 3.3%

Operations7.5K · 3.3%

Join keys

fill rate

Contact LinkedIn93%

Contact name100%

Email59%

Records measured 2,770,266+Contacts reached 226,232+Full schema & examples →

See the real records for yourself.

See full coverage & segmentation across all 35 signal types →

Worked Example

One signal, traced to outcome

One Twitter/X Post 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

Shopify tweets about launching a new AI commerce assistant for merchants, generating 3,400 likes, 890 retweets, and 420K views, tagged as 'Product Launch, AI, E-commerce.'

Action taken

A conversational AI vendor reaches out to Shopify's product team:

“Your AI commerce assistant launch is getting massive traction, with 420K views. We power similar AI assistants for enterprise platforms. Happy to share our merchant-facing AI benchmarks.”

Outcome

Product partnership discussion initiated

Because the outreach referenced a specific, high-performing announcement and offered relevant technical expertise.

Run this against live data →

FAQ

Frequently Asked Questions

What are Twitter/X post signals?

Twitter/X post signals track company tweets with AI-extracted topic tags, initiative detection, pain point identification, and engagement metrics. Autobound monitors 4M+ company Twitter handles on a weekly cadence, classifying tweets into ~300 topic categories and detecting announcements, product launches, and strategic signals that create timely outreach opportunities.

How does Autobound detect Twitter/X post signals?

Autobound monitors company Twitter/X accounts on a weekly refresh cadence. Each tweet is analyzed by AI models that extract topic tags, detect initiatives and pain points, flag competitor mentions, and capture engagement metrics including likes, retweets, and view counts. Signals include direct links to the source tweet and full post text.

How should I use Twitter/X data in my outreach?

Twitter is best for trigger-based outreach around announcements. When a company tweets about a product launch or strategic initiative, reach out within a few days referencing the specific announcement. Keep the tone casual and direct, matching the Twitter platform's conversational style. Focus on high-engagement tweets that represent topics the company is publicly investing attention in.

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 1 signal subtypes with relevance scoring, confidence levels, and entity resolution.

You Receive

Structured JSONL delivered via your preferred method, updated on a weekly 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

Ready to License Twitter/X Post Signals?

Custom pricing based on signal types, delivery frequency, and volume. Full schema documentation and integration guides included.

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