Social Network Analysis for Coordinated Automated Account Detection
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Solution Overview
Problem
Existing methods fail to effectively detect coordinated networks of automated social media posting entities, particularly those attempting to influence stock prices or engage in illegitimate behavior, as they are not specific enough to identify such networks.
Innovation Solution
A system and method for detecting automated account networks using computational devices that analyze posting patterns, follower relationships, and network interactions to identify suspicious entities, employing statistical measurements and machine learning algorithms to categorize accounts as human or automated.
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 coordinated bot networks
Solution Approach 1:
The system segments bot detection into multiple specialized components: individual bot detection algorithms analyze posting behavior patterns, network analysis algorithms examine follower relationships and account connections, and coordination detection algorithms identify synchronized activities. This segmentation allows each component to focus on specific detection aspects, improving overall precision without requiring a single overly complex system
Solution Approach 2:
The system merges multiple detection algorithms and analysis methods into a unified detection framework that combines individual bot detection, network structure analysis, and coordination pattern recognition. This integration enables the system to detect bot networks by synthesizing evidence from multiple sources, achieving high detection precision while managing complexity through modular architecture
2Reliability
If comprehensive network analysis is performed to detect bot networks, then detection accuracy improves, but computational time and resources increase
Solution Approach 1:
The system performs preliminary filtering and analysis by first identifying suspicious accounts using individual bot detection algorithms before applying more computationally intensive network analysis. This preliminary action reduces the scope of subsequent network analysis to only potentially suspicious accounts and their connections, maintaining high detection reliability while significantly reducing computational time and resources
Solution Approach 2:
The system applies different levels of analysis intensity to different parts of the network based on suspicion scores and network importance. High-priority targets with suspected bot network connections receive comprehensive network analysis, while lower-priority accounts receive lighter analysis. This local quality approach ensures reliable detection of critical bot networks while optimizing resource allocation and reducing overall computational time
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.