Identifies trending conversations in a niche on Twitter to inform timely content. Twitter trends are 48–72 hour windows — act fast or pivot to the evergreen angle.
Prerequisites
APIFY_TOKEN environment variable set- Optional: Apify MCP server installed
| 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
Progress:
- [ ] Step 1: Search niche keywords + trending signals
- [ ] Step 2: Extract high-engagement tweet clusters
- [ ] Step 3: Identify topic themes and their velocity
- [ ] Step 4: Score content opportunity per topic
- [ ] Step 5: Deliver trending topic brief
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]", "[NICHE] [current_year]"],
"maxItems": 500,
"tweetLanguage": "en",
"since": "[7 days ago]"
}
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": ["B2B SaaS", "#saas", "B2B SaaS 2026"],
"maxItems": 500,
"tweetLanguage": "en"
}'
Step 2: Identify Trending Topics
Group tweets by topic cluster using keyword co-occurrence. For each cluster:
topic_velocity = count_of_tweets_in_cluster
topic_engagement = sum(likeCount + replyCount * 3 + retweetCount * 2) / topic_velocity
Topic freshness:
freshness = proportion of cluster tweets from last 48 hours
Step 3: Score Content Opportunity
opportunity_score = (topic_velocity / 50, max 1) * 0.30
+ (topic_engagement / 100, max 1) * 0.30
+ freshness * 0.20
+ (top_tweet_by_influencer ? 1 : 0) * 0.20
Content angle recommendation by freshness:
- Freshness > 0.7 → "Timely reaction piece / hot take"; publish within 24h
- Freshness 0.3–0.7 → "Analysis / deep dive"; publish within 72h
- Freshness < 0.3 → "Evergreen explainer"; no urgency
Step 4: Edge Cases
- Topic is news event, not evergreen: Flag as
NEWS_REACTIVE — good for social media posts but risky for long-form content investment
- Trending topic is negative controversy: Flag as
RISK_TOPIC; joining controversy can be brand-damaging; present option to "inform from a distance"
- Niche too broad (returns unrelated topics): Add second qualifier — "B2B SaaS growth" not just "SaaS"
- Trending terms are abbreviations or jargon: Define them in output for non-native audience clarity
# Trending Twitter Topics: [NICHE]
Period: [DATE_RANGE] | Tweets analyzed: [N] | Topic clusters identified: [N] | Date: [DATE]
## Top Trending Topics
| # | Topic | Tweets | Avg Engagement | Freshness | Type | Score |
|---|-------|--------|---------------|---------|------|-------|
| 1 | [topic] | [N] | [N] | [X%] | [TRENDING/NEWS/EVERGREEN] | [0.XX] |
## Content Opportunities
### 1. [Topic Name] (Score: [X])
Volume: [N] tweets | Avg engagement: [N] | Freshness: [X%]
Angle: [recommended content format and angle]
Top tweet: @[handle] ([N] likes): "[excerpt]"
### 2. [Topic Name] ...
## Hashtag Map
| Hashtag | Usage Count | Avg Likes | Co-used With |
|---------|------------|-----------|-------------|
Troubleshooting
No trending topics (flat distribution): Niche may not be particularly active on Twitter; try extending to 14-day window or switching to Reddit for content research in this niche.
All topics are political/news: Add niche qualifier more aggressively in search terms; most general news topics will surface on any broad search.
Content idea doesn't fit your format: Trending topics are inputs, not prescriptions — adapt the angle to your format (e.g. a Twitter controversy about pricing → a blog post "How to Communicate Pricing Changes").