building-twitter-prospect-lists

Builds targeted B2B prospect lists from Twitter/X profiles and posts using apidojo's Twitter scrapers on Apify. Triggers when the user asks to: find Twitter users with a specific job title or keyword in bio, build a list of founders or executives on Twitter, find people tweeting about a topic for outreach, identify potential customers on X, scrape Twitter profiles matching an ICP description, find decision-makers in a specific industry on Twitter, or export a list of leads from Twitter bios. Returns name, username, bio, follower count, location, and recent tweet samples per prospect. Ideal for B2B SDRs, growth hackers, founder-led sales teams, and partnership managers.

Searches Twitter/X for profiles matching a target ICP (Ideal Customer Profile) using bio keywords and topic-based tweet search. Delivers a contact-ready list with engagement signals and bio context.

Prerequisites

• APIFY_TOKEN environment variable set
• Optional: Apify MCP server installed

Inputs

Parameter	Type	Required	Default	Notes
searchTerms	array	✅	[]	Twitter advanced search queries (e.g. ["#AI lang:en", "from:NASA"])
sort	string	Optional	Top	Sort order: Latest, Top, or Latest+Top
tweetLanguage	string	Optional	—	ISO 639-1 language code (e.g. en)
maxItems	number	Optional	Unlimited	Maximum tweets to return
onlyVerifiedUsers	boolean	Optional	false	Only tweets from verified users
onlyTwitterBlue	boolean	Optional	false	Only Twitter Blue subscribers
onlyImage	boolean	Optional	false	Only tweets with images
onlyVideo	boolean	Optional	false	Only tweets with videos
onlyQuote	boolean	Optional	false	Only quote tweets
author	string	Optional	—	Filter to a specific author handle
inReplyTo	string	Optional	—	Tweets replying to a specific handle
mentioning	string	Optional	—	Tweets mentioning a specific handle
geotaggedNear	string	Optional	—	Tweets near a location
withinRadius	string	Optional	—	Radius around geotaggedNear
geocode	string	Optional	—	Lat/lng + radius string
placeObjectId	string	Optional	—	Tweets tagged with a place
minimumRetweets	number	Optional	—	Minimum retweet count
minimumFavorites	number	Optional	—	Minimum like count
minimumReplies	number	Optional	—	Minimum reply count
start	string	Optional	—	Tweets after this date (YYYY-MM-DD)
end	string	Optional	—	Tweets before this date (YYYY-MM-DD)
includeSearchTerms	boolean	Optional	false	Add the matched search term to each tweet
customMapFunction	string	Optional	—	JavaScript function to transform each output object

Workflow

text

Progress:
- [ ] Step 1: Define ICP and search strategy
- [ ] Step 2: Run tweet-scraper for keyword/topic tweets
- [ ] Step 3: Extract unique authors from results
- [ ] Step 4: Enrich with twitter-user-scraper for bio + follower data
- [ ] Step 5: Filter, rank, and deliver prospect list

Step 1: Define ICP and Strategy

Ask the user:

• Job title keywords for Twitter bio search (e.g., "Head of Growth", "Founder", "CTO")
• Topic keywords — what topics does the ICP tweet about? (e.g., "SaaS metrics", "PLG", "RevOps")
• Industry signals — keywords that suggest the right industry in bio (e.g., "SaaS", "fintech", "healthcare")
• Follower range (optional) — e.g., 1,000–50,000 (avoids both nobodies and celebrities)
• Location (optional) — e.g., "San Francisco", "London"
• List size — how many prospects needed?

Step 2: Search for Topic-Based Tweets

Search Twitter for tweets about topics your ICP cares about. People who actively tweet about a topic are warmer prospects.

Recommended — run_actor.js (handles waiting, output, and file saving automatically):

bash

# Quick answer (prints table to chat)
node scripts/run_actor.js \
  --actor "apidojo~tweet-scraper" \
  --input '{"param": "value"}'

# Save as CSV
node scripts/run_actor.js \
  --actor "apidojo~tweet-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.csv --format csv

# Save as JSON
node scripts/run_actor.js \
  --actor "apidojo~tweet-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:

text

Tool: apify:run-actor
Actor: "apidojo~tweet-scraper"
Input:
{
  "searchTerms": ["[TOPIC_KEYWORD_1]", "[TOPIC_KEYWORD_2]"],
  "maxItems": 200,
  "tweetLanguage": "en"
}

If Apify MCP is not available:

bash

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "searchTerms": ["[TOPIC_KEYWORD]"],
    "maxItems": 200
  }'

Run for each topic keyword. Collect all author.username values. Deduplicate. This gives you a candidate pool.

Step 3: Enrich Candidates with Profile Data

Take the top 100-200 unique usernames from Step 2. Fetch full profile data to filter by bio keywords and follower count.

If Apify MCP is available:

text

Tool: apify:run-actor
Actor: "apidojo~twitter-user-scraper"
Input:
{
  "usernames": ["[username1]", "[username2]", "..."],
  "maxItems": 100
}

If Apify MCP is not available:

bash

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "usernames": ["[username1]", "[username2]"]
  }'

Step 4: Filter Against ICP Criteria

From profile data, keep only users where ALL of these are true:

1. Bio contains at least one job title keyword OR industry signal keyword
2. Follower count is within the specified range (if given)
3. Location matches (if specified) — check location field
4. Account is not a bot (has profile picture, has >10 tweets, account age >6 months)

Remove:

• Accounts with default profile images
• Accounts with 0 tweets
• Verified mega-influencers (follower count above range)
• Obviously automated accounts

Step 5: Rank and Format

Rank filtered prospects by:

1. Relevance score = number of ICP keywords matched in bio
2. Engagement proxy = (likes + retweets on recent tweets) / follower count

Output Format

text

# Twitter Prospect List: [ICP DESCRIPTION]
Generated: [N] prospects | Filters applied: [summary] | Date: [DATE]

| # | Name | Handle | Followers | Job / Bio | Location | Last Active | Profile |
|---|------|--------|-----------|-----------|----------|-------------|---------|
| 1 | [name] | @[handle] | [N] | [bio excerpt] | [city] | [date] | [url] |
| 2 | [name] | @[handle] | [N] | [bio excerpt] | [city] | [date] | [url] |

## Top 10 Highest-Priority Prospects
1. **@[handle]** — "[bio]" | [N] followers | Recently tweeted about: [topic]
2. **@[handle]** — "[bio]" | [N] followers | Recently tweeted about: [topic]
...

## Notes
- [N] candidates found in topic search
- [N] filtered out (didn't match ICP criteria)
- [N] final prospects delivered
- Engagement signals are 24-48h delayed

Personalizing Outreach

For each top prospect, the recent tweet sample can be used to personalize outreach. Note their recent topics to reference in a first message.

Troubleshooting

Too few results after filtering: Broaden bio keywords (use OR logic, not AND). Try more topic keywords in Step 2. Too many irrelevant accounts: Add industry-specific keywords to bio filter (e.g., require "SaaS" or "B2B" in bio). Location filter not working: Twitter location is self-reported and inconsistent — treat it as a soft signal, not a hard filter.