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finding-designers-and-creatives-on-twitter

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Finds designers and creative professionals to recruit using apidojo's Twitter scrapers on Apify. Triggers when the user asks to: find designers on Twitter for recruiting, discover UX UI or graphic designers to hire on X, find product designers or creative directors from their Twitter profile, identify visual designers by portfolio signals on Twitter, find motion designers illustrators or brand designers for talent sourcing, build a creative recruiting pipeline from social, or find designers posting about job search. Returns handle, name, design discipline (from bio), portfolio link, follower count, and portfolio signals. Ideal for design agencies, product design leads, creative directors, and startup hiring teams.

What it does

Finding Designers And Creative Professionals on Twitter

The skill document

Finding Designers And Creative Professionals on Twitter

Discovers designers and creative professionals 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": ["UX designer", "product designer", "portfolio", "[DISCIPLINE] open to work"],
  "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": ["UX designer", "product designer", "portfolio", "[DISCIPLINE] open to work"], "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: "UX", "UI", "product design", "graphic design", "motion", "brand design", "Figma", "Sketch", "Adobe"

portfolio_link_signal = bio contains figma.com, behance.net, dribbble.com, or personal domain → strong talent signal

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

# Designers And Creative Professionals Candidates: [DESIGN_DISCIPLINE]
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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