tracking-product-launch-buzz-on-twitter

Tracks product launch buzz and reactions on Twitter using apidojo's Twitter Search scraper. Triggers when the user asks to: track product launch buzz on Twitter, monitor Twitter reactions to a product launch, measure product launch sentiment on X, find tweets about a new product release, track how a product launch is being received on Twitter, monitor competitor product announcements on Twitter, or analyze the social media impact of a product launch. Returns tweet volume, sentiment distribution, top voices, geographic spread, and buzz score. Ideal for product marketing teams, PR professionals, and competitive analysts monitoring launches.

Executes tracking product launch buzz 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": ["[PRODUCT] launch", "[PRODUCT] just launched", "new [PRODUCT]", "[PRODUCT] release"],
  "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": ["[PRODUCT] launch", "[PRODUCT] just launched", "new [PRODUCT]", "[PRODUCT] release"], "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: VIRAL (> 1000 mentions/hour) | HIGH_BUZZ (100-1000/hour) | MODERATE (10-100/hour) | LOW (< 10/hour)

Step 4: Score Each Result

text

score = buzz_score = tweet_volume_24h * 0.35 + weighted_sentiment * 0.35 + influencer_mention_count * 0.30

Step 5: Edge Cases

• Product launch tweets spike within 48h then decay — run within 24-72h of launch for peak signal; old launches produce misleading low volume

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

# Tracking Product Launch Buzz 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.