Imported from apidojo-io/apidojo-skills (skills/intent/monitoring-trending-topics-twitter-by-niche/SKILL.md). Install upstream with npx skills add apidojo-io/apidojo-skills --skill monitoring-trending-topics-twitter-by-niche. Copyright stays with the author (Apache-2.0).

Monitoring Trending Topics Twitter By Niche

Executes monitoring trending topics twitter by niche using apidojo scrapers. Part of the apidojo intelligence skills library.

Prerequisites

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

Inputs

Workflow

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):

# 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:

Tool: apify:run-actor
Actor: "apidojo~tweet-scraper"
Input:
{
  "searchTerms": ["[NICHE]", "#[niche]trending", "[NICHE] breaking"],
  "maxItems": 100
}

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": ["[NICHE]", "#[niche]trending", "[NICHE] breaking"], "maxItems": 100}'

Wait for SUCCEEDED. Fetch dataset:

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

Step 3: Classify Results

classification: BREAKING (velocity > 3×) | RISING (1.5-3×) | STEADY (0.8-1.5×) | DECLINING (< 0.8×)

Step 4: Score Each Result

score = trend_velocity = count(tweets_in_last_24h) / count(tweets_in_prior_24h)

Step 5: Edge Cases

• Distinguish trending within the niche from general Twitter trending — verify the topic is genuinely relevant to the niche by checking co-occurring keywords

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

# Monitoring Trending Topics Twitter By Niche
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.