Building a Twitter Industry Watchlist

Identifies highest-signal Twitter accounts in an industry — people whose tweets consistently generate discussion, surface new information, or shape thinking in the space.

Prerequisites

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

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

Workflow

Progress:
- [ ] Step 1: Search for high-engagement industry tweets
- [ ] Step 2: Collect influential account handles
- [ ] Step 3: Enrich and score
- [ ] Step 4: Classify by account type
- [ ] Step 5: Deliver curated watchlist

Step 1: Search Industry Conversations

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

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

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

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

Tool: apify:run-actor
Actor: "apidojo~tweet-scraper"
Input:
{
  "searchTerms": ["[INDUSTRY]", "#[industry]", "[INDUSTRY] trends", "[INDUSTRY] analysis"],
  "maxItems": 500
}

REST API fallback:

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"searchTerms": ["venture capital", "#vc", "VC trends 2026"], "maxItems": 500}'

Collect authors with likeCount + replyCount >= 10 on their industry tweets.

Step 2: Score Influence

signal_score = (retweets / followers * 1000) * 0.35
             + (replies / followers * 1000) * 0.30
             + min(followers / 100000, 1) * 0.20
             + (tweeted_industry_content >= 3 in 30 days ? 1 : 0) * 0.15

Account type from bio:

• FOUNDER: "founder", "CEO", "built"
• INVESTOR: "partner", "VC", "investor"
• ANALYST: "analyst", "researcher", "writer"
• JOURNALIST: known pub or "reporter", "journalist"
• PRACTITIONER: role title at company

Step 3: Edge Cases

• Bot accounts: retweetCount >> likeCount → flag if retweets > 5× likes
• Ambiguous type: Use PRACTITIONER as default when unclear
• Multiple accounts from same company: Keep the most influential one

Output Format

# [INDUSTRY] Twitter Watchlist
Accounts: [N] | Date: [DATE]

## Founders & Operators
| Name | @Handle | Role | Followers | Avg Likes | Signal Score |
|------|---------|------|-----------|----------|-------------|

## Investors & Analysts
| Name | @Handle | Role | Followers | Signal Score |
|------|---------|------|-----------|-------------|

## Press & Media
| Name | @Handle | Publication | Followers | Signal Score |
|------|---------|------------|-----------|-------------|

## How to Create Twitter List
Go to Twitter → Lists → Create List → Add members by username

Troubleshooting

Results are news not insiders: Use #[industry] hashtag to find community members vs. general readers. Too many promotional accounts: Filter accounts where > 50% of tweets include external links. Watchlist too large: Apply score cutoff ≥ 0.60; keep ≤ 40 accounts for daily readability.