X Audience & Creator Credibility Checker

"Who actually gets replies in this niche, rather than just having followers?" "We booked a creator with 200,000 followers and got zero conversions. How do we verify audience credibility before signing the contract?"

Evaluating X (Twitter) talent before booking campaigns requires real metrics, not superficial follower counts. Most brand managers, talent agencies, and media planners need to verify creator authenticity, check historical growth trends, and catch fake followers before committing budget.

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⬇️ Input

The input accepts a list of Twitter/X creator handles (with or without leading @ symbols) or full profile URLs. Every input field carries prefilled defaults so runs start immediately.

{
  "profiles": [
    "nasa",
    "nike",
    "elonmusk"
  ],
  "maxItems": 100
}

Input Field Specification

Field	Type	Description	Default
profiles	Array	List of Twitter/X creator handles or profile URLs to evaluate.	["nasa"]
maxItems	Integer	Maximum number of profiles to fetch and process in a single run.	100

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⬆️ Output

The actor outputs structured records directly to the default dataset. Each record represents a single creator profile indexed by its stable per-row identifier (screen_name).

Output Field Schema

Field Name	Type	Description
screen_name	String	Twitter/X username handle without leading @ symbol. Stable per-row identifier.
name	String	Display name of the X creator.
followers_count	Integer	Total follower count observed on the profile.
friends_count	Integer	Total number of accounts followed by this creator.
statuses_count	Integer	Total number of posts and replies published.
rest_id	String	Numeric X user REST ID for permanent cross-run resolution.
description	String	Profile biography text.
created_at	String	Account creation date timestamp.
verified	Boolean	Verification status flag (true for verified/blue checkmarks).
profile_image_url	String	HTTPS URL of the creator's profile avatar image.
engagementRate	Number | Null	Computed engagement rate relative to peer creators in the same size band. Returns null on first observation with explanatory reason.
followerGrowth30d	Number | Null	Follower growth over the trailing 30 days.
followerLoss30d	Number | Null	Follower loss over the trailing 30 days.
changedFields	Array | Null	List of profile attributes that changed since previous run observation.
reason	String | Null	Explanation note when computed historical metrics return null on first observation.
scrapedAt	String	ISO 8601 timestamp of data collection.

Sample Output Record

{
  "screen_name": "NASA",
  "name": "NASA",
  "followers_count": 92241671,
  "friends_count": 119,
  "statuses_count": 74293,
  "rest_id": "11348282",
  "description": "Making the seemingly impossible, possible. ✨",
  "created_at": "Wed Dec 19 20:20:32 +0000 2007",
  "verified": true,
  "profile_image_url": "https://pbs.twimg.com/profile_images/1321163587679784960/0ZxKlEKB_normal.jpg",
  "engagementRate": null,
  "followerGrowth30d": null,
  "followerLoss30d": null,
  "changedFields": null,
  "reason": "First observation; trend metrics and change tracking require multi-run history accrued over time.",
  "scrapedAt": "2026-08-05T08:30:40.450Z"
}

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How it works

1. Guest Authentication: Initializes lightweight guest session tokens directly with X endpoints without requiring user login credentials or browser automation.
2. GraphQL Profile Extraction: Queries native user GraphQL endpoints to pull complete profile metadata, verification badges, numerical account IDs (rest_id), and historical creation timestamps.
3. Structured Metrics & Compute Layer: Normalizes numerical fields, checks for profile modifications, and computes derived fields. On first observation, trend fields return null accompanied by an explicit reason string rather than estimated guesses.
4. Resilient Handling: Automatically detects token expiry, refreshes guest tokens seamlessly, and retries requests without failing the run.

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❓ FAQ

How does audience credibility assessment work?

Audience credibility and authenticity signals rely on inspecting account age (created_at), ratio of followers to following (followers_count vs friends_count), overall posting activity (statuses_count), and tracking profile changes across repeated runs over time.

Why do engagementRate and followerGrowth30d return null on first run?

Derived historical metrics like follower growth, decline, and change tracking require observing the same handle across multiple runs over time. On the first observation, the actor returns null with a clear explanatory reason field rather than fabricating unobserved historical figures.

Does this actor require Twitter/X login credentials or cookies?

No. The actor operates entirely without user login credentials, cookies, or browser instances, using guest token authorization routines.

Can I run bulk checks on large creator rosters?

Yes. Pass an array of creator handles in the profiles input parameter. You can cap total processing using the maxItems setting.

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💬 Your feedback

Need additional computed metrics, specialized export formats, or dataset integrations? Contact us at reapxdev@proton.me or visit reapx.dev.

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Unofficial - not affiliated with X (Twitter). Collects data without login. reapx. Contact reapxdev@proton.me.