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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_TOKENenvironment 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 watchlistStep 1: Search Industry Conversations
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~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 jsonAPIFY_TOKENmust be set in environment or.envfile.
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:
bash
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) * 0.15Account 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
PRACTITIONERas 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 usernameTroubleshooting
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.
