monitoring-vc-investor-activity-on-twitter

Monitors VC investor activity and deal signals on Twitter using apidojo's Tweet scraper. Triggers when the user asks to: monitor VC activity on Twitter, track what investors are tweeting about, find VCs who are actively investing in a sector on X, monitor deal flow signals from investor tweets, find investors who are looking for deals in a space, track funding announcements from VC firms on Twitter, or discover which investors are most active in a specific vertical. Returns investor handle, firm, investment thesis signals, recent activity, deal flow indicators. Ideal for startup founders fundraising, co-investors, and startup ecosystem analysts.

Executes monitoring vc investor activity on twitter using apidojo scrapers. Part of the apidojo intelligence skills library.

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 parameters
- [ ] Step 2: Run tweet-scraper
- [ ] Step 3: Filter and classify results
- [ ] Step 4: Score by quality and relevance
- [ ] Step 5: Deliver output

Step 2: Run the Actor

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": ["investing in [SECTOR]", "excited about [SECTOR]", "looking for [SECTOR] startups", "portfolio company [SECTOR]"],
  "maxItems": 100
}

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": ["investing in [SECTOR]", "excited about [SECTOR]", "looking for [SECTOR] startups", "portfolio company [SECTOR]"], "maxItems": 100}'

Wait for SUCCEEDED. Fetch dataset:

bash

curl "https://api.apify.com/v2/actor-runs/$RUN_ID/dataset/items?token=$APIFY_TOKEN"

Step 3: Classify Results

text

classification: ACTIVE_INVESTOR (recent deal activity, thesis tweets) | PASSIVE_FOLLOWER (investor title but no deal signals) | ANGEL (individual, smaller checks) | FIRM_PARTNER (major VC firm affiliation)

Step 4: Score Each Result

text

score = deal_signal_score = (tweeted_investment_thesis ? 1 : 0) * 0.35 + (tweeted_about_portfolio ? 1 : 0) * 0.25 + (bio_contains_VC_firm ? 1 : 0) * 0.25 + (followerCount > 5000 ? 1 : 0.5) * 0.15

Step 5: Edge Cases

• Distinguish actual investment partners from associates and VPs — partners make investment decisions; use title from bio for classification

Additional fallbacks:

• < 20 results: Broaden search terms; remove secondary filters
• No results: Verify the search terms are correct; try alternate phrasings
• Data quality issues: Remove entries with missing key fields; note count in output

Output Format

text

# Monitoring Vc Investor Activity On Twitter
Results: [N] | Date: [DATE]

| # | [Key Field] | [Metric 1] | [Metric 2] | [Classification] | [Score] |
|---|------------|-----------|-----------|-----------------|---------|
| 1 | [value] | [value] | [value] | [type] | [0.XX] |

## Summary
Top result: [description]
Key finding: [insight]

Troubleshooting

Too few results: Broaden the primary search term; remove restrictive filters. Low quality results: Apply minimum score threshold (≥ 0.50) to filter noise. Actor fails to run: Verify API key; check actor status at apify.com/apidojo.