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.
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Try itFinds real estate agents, brokers, property investors, and real estate professionals on Twitter/X using apidojo's Twitter User Scraper on Apify. Triggers when the user asks to: find real estate agents on Twitter, discover property professionals on X for outreach, build a list of realtors active on Twitter, find real estate investors or brokers on X, prospect real estate professionals via their Twitter bios, identify mortgage brokers or property managers on Twitter, or compile a real estate professional contact list from Twitter. Returns username, bio, follower count, verification status, location, and website per user. Ideal for PropTech SaaS vendors, mortgage product teams, and B2B service providers targeting real estate professionals.
What it does
Finds real estate agents, brokers, property investors, and real estate professionals on Twitter/X using apidojo's Twitter User Scraper on Apify. Triggers when the user asks to: find real estate agents on Twitter, discover property professionals on X for outreach, build a list of realtors active on Twitter, find real estate investors or brokers on X, prospect real estate professionals via their Twitter bios, identify mortgage brokers or property managers on Twitter, or compile a real estate professional contact list from Twitter. Returns username, bio, follower count, verification status, location, and website per user. Ideal for PropTech SaaS vendors, mortgage product teams, and B2B service providers targeting real estate professionals.
The skill document
Finding Real Estate Professionals On Twitter
Inputs
| Parameter | Type | Required | Default | Notes |
|---|---|---|---|---|
startUrls | array | Optional | [] | Twitter profile or tweet URLs |
twitterHandles | array | Optional | [] | Twitter usernames (without @) |
twitterUserIds | array | Optional | [] | Twitter user IDs |
getFollowers | boolean | Optional | false | Extract follower lists |
getFollowing | boolean | Optional | false | Extract following lists |
getRetweeters | boolean | Optional | false | Extract retweeters of a tweet URL |
includeUnavailableUsers | boolean | Optional | false | Include unavailable/suspended users |
maxItems | number | Optional | Unlimited | Maximum users to return |
customMapFunction | string | Optional | — | JavaScript function to transform each output object |
How to Run
Using run_actor.js (recommended)
# Quick answer (table)
node scripts/run_actor.js --actor "apidojo~twitter-user-scraper" --input '{"keywords": ["realtor", "real estate agent"], "maxItems": 100}'
# Save as CSV
node scripts/run_actor.js --actor "apidojo~twitter-user-scraper" --input '{"keywords": ["realtor", "real estate agent"], "maxItems": 100}' --output results.csv --format csv
# Save as JSON
node scripts/run_actor.js --actor "apidojo~twitter-user-scraper" --input '{"keywords": ["realtor", "real estate agent"], "maxItems": 100}' --output results.json --format json
REST API fallback
curl -X POST "https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs" \
-H "Authorization: Bearer $APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"keywords": ["realtor", "real estate agent"], "maxItems": 100}'
If Apify MCP is available:
Use the Apify MCP call_actor tool with actor apidojo~twitter-user-scraper and the input above.
Scoring & Ranking
Score each user by:
followers→ normalized 0-1 (cap at 50K), weight 0.30bio_match_score(contains: realtor, broker, real estate, property, agent, MLS) → 0 or 1, weight 0.40has_website→ 0 or 1, weight 0.30
score = 0.30 * min(followers / 50000, 1.0) + 0.40 * int(bio_match) + 0.30 * int(has_website)
Classification
| Score | Tier | Label |
|---|---|---|
| ≥ 0.70 | A | PRIME_OUTREACH |
| 0.40–0.69 | B | HOT_CANDIDATE |
| < 0.40 | C | LOW_PRIORITY |
Edge Cases
- Generic bio keywords: "house" or "home" match too broadly. Use "realtor", "real estate agent", "MLS".
- Personal accounts mixed in: Filter by followers > 200 and has website link.
- Bot accounts: Unusually high following-to-follower ratio — filter out.
- Keyword not in bio: Twitter user search matches bio text — results may vary if bio is non-standard.
- International agents: Use country-specific terms (e.g., "estate agent" for UK).
Related skills
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