tracking-twitter-thought-leaders

Identifies and tracks thought leaders and key voices in any industry on Twitter/X using apidojo's scrapers. Triggers when the user asks to: find the top voices in an industry on Twitter, identify thought leaders in a niche, discover who has the most influence in a topic area on X, find experts tweeting about a subject, build a list of influencers to engage with on Twitter, track who is gaining followers fastest in a category, or identify key opinion leaders in a field for PR or partnership outreach. Returns name, handle, follower count, engagement rate, bio keywords, and recent top tweets. Ideal for PR teams, community managers, and B2B content marketers.

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

text

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:

text

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:

text

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:

text

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

text

# 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.