Click-Farming Detection Using User and Population Density Ratios
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Solution Overview
Problem
Existing methods for determining click-farming in live rooms are single and inaccurate, leading to errors in identifying fraudulent activities on livestreaming platforms.
Innovation Solution
A method that collects user distribution information and determines a user distribution ratio, combined with obtaining population density distribution information and determining a population density distribution ratio, to assess whether click-farming exists in a live room.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single method (total person quantity monitoring) is used to determine click-farming, then the determination process is simple, but the accuracy of determining click-farming is low
Solution Approach 1:
The patent segments the single total person quantity metric into multiple independent distribution dimensions: user distribution (geographic locations), device distribution (terminal types), and network distribution (IP addresses). Each dimension is analyzed separately to detect anomalies, thereby improving measurement precision without creating a monolithic complex system.
Solution Approach 2:
The patent transitions from one-dimensional monitoring (total person quantity) to multi-dimensional analysis by introducing geographic location, terminal type, and IP address as additional dimensions. This dimensional expansion enables more accurate click-farming detection by identifying inconsistencies across multiple axes simultaneously.
2Measurement precision
If multi-dimensional user distribution analysis is implemented, then the accuracy of determining click-farming is improved, but the complexity of the determination method increases
Solution Approach 1:
The patent creates a universal determination framework that handles multiple distribution dimensions (user, device, network) through a single integrated system. This multi-functional approach improves accuracy by analyzing all dimensions simultaneously while avoiding the need for separate complex systems for each dimension.
Solution Approach 2:
The patent merges the analysis of user distribution, device distribution, and network distribution into a unified determination process. By combining these dimensions and comparing their consistency, the system achieves high accuracy without requiring three separate complex determination systems.
3Measurement precision
If real-time user distribution monitoring is performed, then the accuracy of detecting click-farming is improved, but the computational resources and time required increase
Solution Approach 1:
The patent performs preliminary actions by pre-collecting and organizing user distribution data, device distribution data, and network distribution data before the actual determination process. This preparation reduces the computational burden during real-time detection, maintaining high accuracy while reducing determination time.
Solution Approach 2:
The patent replaces complex mechanical computation with optimized data processing methods. By using efficient algorithms to analyze distribution patterns and compare consistency across dimensions, the system achieves high detection accuracy with reduced computational time and resource consumption.
Data Source
AI summary
This application provides a method and an apparatus for determining click-farming in a live room. The method for determining click-farming in a live room includes: determining user distribution information associated with a target live room, and determining a user distribution ratio based on the user distribution information; obtaining population density distribution information, and determining a population density distribution ratio based on the population density distribution information; and determining whether click-farming exists in the target live room based on the user distribution ratio and the population density distribution ratio. This method enriches means for determining click-farming in the live room, and effectively improves accuracy of determining click-farming.