Currently, an increasing number of online activities are not initiated by real users, but by scripts, emulators, crawlers, or bulk-controlled accounts. Whether registering on platforms like ChatGPT, Facebook, TikTok, or Google, any "environmental simulation" operation is difficult to avoid detection by the platform's risk control system.

I. Typical Characteristics of Non-Organic Users

Non-organic users often use technical means to simulate, fabricate, or bulk-manipulate user behavior. Their main goals include:

Bulk registration: Automated account creation for subsequent account maintenance or "wool-pulling" activities.

Task inflating: Such as inflating likes, comments, or downloads.

Ad fraud: Simulating ad clicks and falsifying conversion data.

Data scraping: Bulk content capture by simulating user requests.

Risk control bypassing: Using proxies, emulators, or device masquerading techniques to evade platform detection.

These behaviors often reveal themselves in subtle details, and anti-fraud systems detect them through multi-dimensional analysis.

II. The Core Principle of Identifying "Non-Organic Users"

The essence of anti-fraud lies in multi-dimensional feature modeling and abnormal behavior identification. The platform system typically analyzes the following four dimensions simultaneously within milliseconds:

1. Behavioral Fingerprints

Human behavior is natural and random, while automated behavior exhibits high efficiency and strong regularity.

Abnormal Operation Rhythm

For example, completing registration within 200 milliseconds or payment in seconds hardly conforms to human operating habits.

Different Interaction Trajectories

Mouse trajectories: Humans have smooth and slightly jittery trajectories, while scripts tend to be nearly straight.

Slider Verification: Humans have nonlinear sliding curves, while scripts often exhibit a constant sliding speed.

Single Functionality

For example, accounts that only log in but don't browse, or only like but don't comment, lack a natural functional combination.

Technical Implementation: Based on statistical models and anomaly detection algorithms (such as Z-score, IQR, and Isolation Forest).

2. Device & Environment Fingerprinting

Non-natural users often use virtual machines, emulators, multi-opening tools, or device camouflage techniques.

Device Fingerprint Collection

Includes browser user identity (UA), fonts, canvas fingerprints, and WebGL hashes; mobile devices include IMEI, IDFA, Android ID, and MAC address.

Environmental Consistency Verification

UA claims to be an iPhone 15, but the graphics card fingerprint shows NVIDIA → Highly suspicious.

Emulator Detection: Identify virtual environments based on CPU model, driver signature, and memory distribution.

Device Overlap Detection

Registering a large number of accounts on the same device in a short period of time is extremely risky.

3. Network & Geo Analysis

A large number of non-organic users rely on proxy pools, VPNs, cloud servers, or data center IPs.

IP Anomalies

A large number of registrations from the same IP in a short period of time.

Using suspicious cloud service IPs or residential proxy segments.

Frequent cross-region logins

For example, being in New York one minute ago and logging in from Shanghai one minute later.

Geographic Location Cross-Verification

GPS location and IP location are inconsistent.

The same device claims to be from multiple different cities.

4. Graph-based Detection

Identifying non-organic users requires not only focusing on individual behavior but also identifying clusters of accounts that are controlled in bulk.

Device-Account-IP Triple Analysis

A device is associated with a large number of accounts, or multiple accounts are logged in from the same IP address.

Behavioral Correlation Graph

Use graph algorithms to analyze behaviors such as likes, comments, and transactions to identify highly homogenous groups.

Identifying "Goldie Pants"

Bulk accounts using the same coupon at the same time and through the same channel → Extremely high risk.

3. How to Reduce False Positive Identification of "Non-organic Users"?

To build a high-success access environment, the key is to avoid triggering risk control features, not "circumventing the rules."

Recommended strategies include:

Use trusted, geo-targeted residential proxy IP addresses to avoid the high block rate of data center IP addresses.

Ensure a consistent device environment, including language, system fingerprint, time zone, and resolution.

Control the operation rhythm to simulate human behavior (e.g., click intervals, swipe paths, and wait times).

Implement a "one account, one environment" strategy to avoid reusing IP addresses, device fingerprints, or session information.

For multi-account deployments, we recommend using configuration software + stable IP addresses + cookie isolation.

Using a dedicated residential proxy IP (such as the one provided by OwlProxy) is a more stable and reliable option, helping you successfully pass platform risk control checks.

Risk control systems are becoming increasingly intelligent, not only identifying technical behavior but also building a "personality profile behind the behavior." Only by simulating real user behavior and environments as closely as possible can we effectively reduce the risk of being identified as a "non-organic user" by the system.