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finding-trending-twitter-topics-for-content

Name: finding-trending-twitter-topics-for-content
Author: apidojo-io

Finds trending Twitter topics and conversations for content ideation using apidojo's Twitter scrapers on Apify. Triggers when the user asks to: find trending topics on Twitter for content, discover what is being discussed in a niche on X right now, identify Twitter conversations to join with content, find trending hashtags in an industry on Twitter, research what topics are generating engagement in a space on X, discover viral tweet themes for blog or video content, or find what your target audience is talking about on Twitter this week. Returns trending topics, tweet volume signals, top engagement posts, and content angle suggestions. Ideal for content marketers, social media managers, newsletter writers, and real-time content teams.

What it does

Finding Trending Twitter Topics for Content

The skill document

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

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

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

Step 1: Search Tweets

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"
  }'

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

Output Format

# 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").

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What it does

The skill document