---name: finding-real-estate-professionals-on-twitterdescription: >Twitter/X using apidojo's Twitter User Scraper on Apify. Triggers when the user asks to: findlicense: Apache-2.0metadata: author: apidojoversion: 1.0apify-actor: apidojo/twitter-user-scraper---

Finding Real Estate Professionals On Twitter

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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.

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Scoring & Ranking

Score each user by:

• followers → normalized 0-1 (cap at 50K), weight 0.30
• bio_match_score (contains: realtor, broker, real estate, property, agent, MLS) → 0 or 1, weight 0.40
• has_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)

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Classification

Score	Tier	Label
≥ 0.70	A	PRIME_OUTREACH
0.40–0.69	B	HOT_CANDIDATE
< 0.40	C	LOW_PRIORITY

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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).