finding-real-estate-professionals-on-twitter

核心功能：借助Apify推特用户爬虫，通过关键词、账号ID等条件，精准定位房产经纪人、投资者等从业者，还会按粉丝数、Bio匹配度等评分分级，支持导出CSV/JSON。典型场景：适合PropTech厂商、抵押产品团队开展B2B获客。输入为搜索参数，输出含用户名、粉丝数、认证状态、联系方式等信息。

apidojo-io

热度 7.62

最近更新 2026-09-03 04:46:01

分类：营销依赖：第三方 API / MCP 服务 / 代码执行应用场景：市场分析 / 数据处理 / 社交媒体

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add finding-real-estate-professionals-on-twitter

Metadata

name: finding-real-estate-professionals-on-twitter
description: 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.
license: Apache-2.0
metadata: {"author":"apidojo","version":"1.0","apify-actor":"apidojo/twitter-user-scraper"}

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 0.30
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

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).