tracking-twitter-thought-leaders skill by apidojo-io

Tracking Twitter Thought Leaders

Finds Twitter/X accounts with genuine influence in a topic area — not just high follower counts, but accounts whose tweets get shared and discussed. Delivers a ranked list for PR outreach, community engagement, or partnership targeting.

Prerequisites

APIFY_TOKEN environment variable set
Optional: Apify MCP server installed

Inputs

Parameter	Type	Required	Default	Notes
`startUrls`	array	Optional	`[]`	Twitter profile or tweet URLs
`twitterHandles`	array	Optional	`[]`	Twitter usernames (without @)
`twitterUserIds`	array	Optional	`[]`	Twitter user IDs
`getFollowers`	boolean	Optional	`false`	Extract follower lists
`getFollowing`	boolean	Optional	`false`	Extract following lists
`getRetweeters`	boolean	Optional	`false`	Extract retweeters of a tweet URL
`includeUnavailableUsers`	boolean	Optional	`false`	Include unavailable/suspended users
`maxItems`	number	Optional	Unlimited	Maximum users to return
`customMapFunction`	string	Optional	—	JavaScript function to transform each output object

Workflow

Progress:
- [ ] Step 1: Define topic, industry, and influence criteria
- [ ] Step 2: Search for topic-relevant tweets to find active voices
- [ ] Step 3: Enrich top accounts with profile data
- [ ] Step 4: Score by influence signals
- [ ] Step 5: Deliver ranked thought leader list

Step 1: Clarify Parameters

Ask the user for:

Topic or industry (e.g., "AI safety", "B2B SaaS growth", "climate tech")
Influence type — broad reach (high followers), community depth (high engagement), or rising voices (growing fast)
Follower range (default: 5,000–2,000,000 — excludes unknown accounts and mega-celebrities)
Geography/language (optional)
List size (default: 25)

Step 2: Search for Topic Tweets

Find who's actively tweeting about the topic — recent activity matters more than old follower counts.

Recommended — run_actor.js (handles waiting, output, and file saving automatically):

# Quick answer (prints table to chat)
node

APIFY_TOKEN must be set in environment or .env file.

If Apify MCP is available:

REST API fallback:

Extract unique author.username values from all results. Sort by their tweet's retweet+like count — accounts whose topic tweets get the most engagement are the most influential voices.

Step 3: Enrich with Profile Data

Take top 100 candidate usernames. Fetch full profiles.

If Apify MCP is available:

REST API fallback:

Step 4: Score by Influence

Calculate composite influence score for each account:

Filter: keep only accounts within follower range AND whose bio suggests topical relevance.

Step 5: Format Output

Output Format

Troubleshooting

Results dominated by one person: Some topics have one mega-voice. Exclude them and surface the next tier. Not enough topically relevant accounts: Expand keyword list with synonyms, adjacent topic terms, and industry jargon. Follower counts seem off: Cached data — for final list, spot-check top 5 accounts directly on Twitter.

tracking-twitter-thought-leaders

Documentation

Documentation