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finding-marketing-professionals-on-twitter

试用

Finds marketing professionals and growth specialists to recruit using apidojo's Twitter scrapers on Apify. Triggers when the user asks to: find marketing professionals on Twitter for recruiting, discover growth marketers or CMOs to hire from X, find content marketers SEO specialists or paid acquisition managers on Twitter, identify demand generation or lifecycle marketing managers via social, find marketing leads or VP Marketing candidates from Twitter, build a marketing recruiting pipeline from social, or find marketers posting about career moves. Returns handle, name, marketing specialty (from bio), follower count, content signals, and open-to-work indicators. Ideal for startup marketing hiring managers, growth-stage companies, and executive recruiting firms.

它能做什么

Finding Marketing Professionals And Growth Specialists on Twitter

技能文档

Finding Marketing Professionals And Growth Specialists on Twitter

Discovers marketing professionals and growth specialists on Twitter via skill keywords, portfolio/project signals, and open-to-work indicators. Twitter surfaces professionals who actively discuss their craft — a strong passive candidate signal.

Prerequisites

  • APIFY_TOKEN environment variable set
  • Optional: Apify MCP server installed

Inputs

ParameterTypeRequiredDefaultNotes
startUrlsarrayOptional[]Twitter profile or tweet URLs
twitterHandlesarrayOptional[]Twitter usernames (without @)
twitterUserIdsarrayOptional[]Twitter user IDs
getFollowersbooleanOptionalfalseExtract follower lists
getFollowingbooleanOptionalfalseExtract following lists
getRetweetersbooleanOptionalfalseExtract retweeters of a tweet URL
includeUnavailableUsersbooleanOptionalfalseInclude unavailable/suspended users
maxItemsnumberOptionalUnlimitedMaximum users to return
customMapFunctionstringOptional—JavaScript function to transform each output object

Workflow

Progress:
- [ ] Step 1: Search for role-specific tweets
- [ ] Step 2: Collect unique handles
- [ ] Step 3: Enrich profiles
- [ ] Step 4: Score candidate fit
- [ ] Step 5: Deliver candidate list

Step 1: Search Queries

Recommended — run_actor.js (handles waiting, output, and file saving automatically):

# Quick answer (prints table to chat)
node scripts/run_actor.js \
  --actor "apidojo~twitter-user-scraper" \
  --input '{"param": "value"}'

# Save as CSV
node scripts/run_actor.js \
  --actor "apidojo~twitter-user-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.csv --format csv

# Save as JSON
node scripts/run_actor.js \
  --actor "apidojo~twitter-user-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.json --format json

APIFY_TOKEN must be set in environment or .env file.

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~tweet-scraper"
Input:
{
  "searchTerms": ["growth marketer", "VP Marketing", "marketing open to work", "content marketing jobs"],
  "maxItems": 300,
  "tweetLanguage": "en"
}

REST API fallback:

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"searchTerms": ["growth marketer", "VP Marketing", "marketing open to work", "content marketing jobs"], "maxItems": 300}'

Collect unique author.username from results.

Step 2: Enrich Profiles

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~twitter-user-scraper"
Input: {"usernames": ["[username1]", "[username2]", "..."]}

REST API fallback:

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"usernames": ["handle1", "handle2"]}'

Step 3: Filter and Score

Skill confirmation: bio contains keywords: "growth", "marketing", "CMO", "demand gen", "content", "SEO", "paid media", "lifecycle", "GTM"

thought_leader_signal = followerCount > 1000 AND tweets in last 30 days about marketing topics

Candidate score:

candidate_score = (skill_confirmed ? 1 : 0) * 0.35
                + (open_to_work_signal ? 1 : 0) * 0.30
                + (followerCount in 200..20000 ? 1 : 0.6) * 0.20
                + (tweeted_in_last_30_days ? 1 : 0) * 0.15

Activity: active (< 30 days) | passive (30–90 days) | dormant (> 90 days)

Step 4: Edge Cases

  • Company/brand accounts in results: Filter where followerCount > 50K AND bio contains no personal pronouns; these are likely brand accounts
  • < 20 candidates found: Broaden skill term; remove location or seniority filter; try adjacent skills
  • Bot detection: Flag followerCount / followingCount < 0.05 AND tweetsCount < 20 as potential bot
  • Location not matching: Bio location is free text — use fuzzy match; accept partial city/country names

Output Format

# Marketing Professionals And Growth Specialists Candidates: [MARKETING_SPECIALTY]
Profiles found: [N] | Open-to-work: [N] | Active: [N] | Date: [DATE]

## Priority: Open-to-Work Candidates
| Name | @Handle | Specialty | Location | Followers | Last Active | Score |
|------|---------|----------|---------|-----------|------------|-------|

## Passive Candidates
| Name | @Handle | Specialty | Location | Followers | Score |
|------|---------|----------|---------|-----------|-------|

## Bio Highlights (Top 5)
1. @[handle]: "[bio excerpt]"

Troubleshooting

All results are agencies/companies not individuals: Add personal pronouns filter or search "I am a [role]", "I do [skill]". Role too generic returns too many results: Add location OR seniority qualifier. No open-to-work signals: Most candidates don't signal publicly — treat passive candidates as warm leads with personalized outreach referencing their recent content.

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