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):

bash

# Quick answer (prints table to chat)
node scripts/run_actor.js \
  --actor "apidojo~twitter-user-scraper" \
  --input '{"param": "value"}'

# Save as CSV
node scripts/run_actor.js \
  --actor "apidojo~twitter-user-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.csv --format csv

# Save as JSON
node scripts/run_actor.js \
  --actor "apidojo~twitter-user-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": ["[TOPIC_KEYWORD_1]", "[TOPIC_KEYWORD_2]", "[TOPIC_KEYWORD_3]"],
  "maxItems": 300,
  "tweetLanguage": "en"
}

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": ["[TOPIC_KEYWORD_1]", "[TOPIC_KEYWORD_2]"],
    "maxItems": 300
  }'

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:

Tool: apify:run-actor
Actor: "apidojo~twitter-user-scraper"
Input:
{
  "usernames": ["[username1]", "[username2]", "...up to 100"]
}

REST API fallback:

bash

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"usernames": ["[username1]", "[username2]"]}'

Step 4: Score by Influence

Calculate composite influence score for each account:

topic_engagement = avg(likes + retweets) on topic-related tweets
audience_quality = followers / following ratio (>1 is healthy)
influence_score = topic_engagement * log(followers) * audience_quality

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

Step 5: Format Output

Output Format

# Twitter Thought Leaders: [TOPIC/INDUSTRY]
Accounts analyzed: [N] | Final list: [N] | Date: [DATE]

## Top Thought Leaders

| # | Name | @Handle | Followers | Influence Score | Bio Excerpt | Recent Top Tweet |
|---|------|---------|-----------|-----------------|-------------|------------------|
| 1 | [name] | @[handle] | [N] | [score] | [bio] | "[tweet excerpt]" |

## Tier Breakdown

### 🏆 Power Voices (500K+ followers)
[list with brief bio and latest relevant tweet]

### 🎯 Core Influencers (50K–500K followers)
[list — best for outreach: big enough to matter, accessible enough to respond]

### 🌱 Rising Voices (5K–50K followers)
[list — early partnership opportunity, lower cost, high engagement]

## Best Accounts for Direct Outreach
[Top 5 picks with rationale — why they're ideal for PR, partnership, or co-content]

## Content Themes These Voices Tweet About
- [Theme 1]: [N] of the accounts tweet regularly about this
- [Theme 2]: [N] accounts

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

NPX Install

Tags

SKILL.md Content

Tracking Twitter Thought Leaders

Prerequisites

Inputs

Workflow

Step 1: Clarify Parameters

Step 2: Search for Topic Tweets

Step 3: Enrich with Profile Data

Step 4: Score by Influence

Step 5: Format Output

Output Format

Troubleshooting