Social Network Bot Detection via Posting Pattern Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods fail to effectively detect automated account networks on social media, which can engage in malicious activities such as influencing stock prices, as they are not specific enough to identify bot networks.
Innovation Solution
A system and method that uses a computational device to analyze social media data, identifying suspicious entities by their posting and follower patterns, and determining network relationships to detect and flag automated accounts, employing filters and scoring systems to differentiate between human and automated behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If general bot detection methods are used, then detection coverage is provided, but detection precision is insufficient to correctly identify bot networks
Solution Approach 1:
The system segments the detection process into multiple specialized modules: entity extraction module, network relationship determination module, bot detection module, and amplifier detection module. Each module handles a specific aspect of the detection task, improving precision through specialized analysis while managing complexity through modular design.
Solution Approach 2:
The system transitions from analyzing individual posting entities in isolation to analyzing network relationships between entities. By adding the network dimension (followers, followings, interactions), the system achieves higher detection precision for bot networks rather than just individual bots.
2Reliability
If network relationship analysis is performed on all entities, then bot network detection accuracy improves, but computational complexity increases
Solution Approach 1:
The system applies different analysis depths to different entities based on their characteristics. High-scoring suspicious entities undergo comprehensive network relationship analysis, while low-scoring entities receive minimal analysis. This local differentiation improves reliability for critical detections while reducing overall computational complexity.
Solution Approach 2:
The system performs preliminary bot detection scoring on entities before conducting full network relationship analysis. This preliminary filtering action identifies high-priority suspects that warrant comprehensive network analysis, reducing the computational burden by focusing detailed analysis only on relevant cases.
3Measurement precision
If sophisticated filtering and scoring systems are implemented, then detection precision improves, but ease of operation decreases
Solution Approach 1:
The system automatically adjusts detection thresholds and parameters based on the analyzed data characteristics. The sophisticated filtering and scoring systems self-optimize without requiring manual configuration, maintaining high detection precision while preserving ease of operation through automated adaptation.
Data Source
AI summary
A system and method for detecting interactive network of automated accounts, the interactive network of automated accounts comprising a plurality of automated accounts posting to a social media channel, the system comprising: an ingestion engine operated by a computational device for connecting to the social media channel and receiving a plurality of social media postings from a plurality of posting entities; a bot model operated by a computational device for determining whether at least one posting entity is a suspected bot; and a computer network for communication between said computational devices.