finding-real-estate-professionals-on-twitter

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: fin…

API Dojo

@apidojo-io

Install

$ openclaw skills install @apidojo-io/finding-real-estate-professionals-on-twitter

Finding Real Estate Professionals On Twitter


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
customMapFunctionstringOptionalJavaScript 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 0.30
  • 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)

Classification

ScoreTierLabel
≥ 0.70APRIME_OUTREACH
0.40–0.69BHOT_CANDIDATE
< 0.40CLOW_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).

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