Bot Detection System for Social Media Data Credibility
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Solution Overview
Problem
Social media data is contaminated with noise from automated programs (bots) that generate spam, phishing attacks, and unsolicited advertisements, making it challenging for social media analytics to provide credible results.
Innovation Solution
A system that assigns a likelihood score to each user as either human or bot based on statistical, temporal, and text features from social media posts, interactions, and historical profile information, using dimension reduction techniques and classification methods to identify and filter out bots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If social media analytics algorithms analyze all social media data, then the volume of analyzed data increases, but the credibility and accuracy of analysis results deteriorate due to noise from bot-generated content
Solution Approach 1:
The patent extracts and removes bot-generated content from the social media data stream by identifying characteristic bot features (posting frequency, content patterns, interaction behaviors) and filtering them out before analysis, thereby preserving the volume of human-generated data while eliminating the harmful noise that degrades credibility
Solution Approach 2:
The patent introduces an intermediary bot detection and classification system that sits between data collection and analytics processing. This intermediary layer classifies users as bot or human based on multiple features and filters bot content before it reaches the analytics algorithms, preventing noise from contaminating the analysis results
2Reliability
If bot detection filters are applied to social media data, then the credibility of data improves, but the complexity of the data processing system increases
Solution Approach 1:
The patent segments the bot detection process into distinct modular components: feature extraction modules that capture different aspects of user behavior, classification modules that analyze specific feature patterns, and filtering modules that remove identified bot content. This segmentation allows each component to be optimized independently and simplifies the overall system architecture
Solution Approach 2:
The patent changes the parameters of data processing by focusing on a selective set of critical features (posting frequency, content length, interaction patterns, temporal patterns) rather than analyzing all possible data attributes. This parameter selection reduces computational complexity while maintaining high detection accuracy
3Measurement precision
If multiple features are analyzed to detect bots, then the precision of bot identification improves, but the computational time and resources increase
Solution Approach 1:
The patent applies partial action by implementing a two-stage detection process: first analyzing a subset of high-weight features to identify obvious bots, then analyzing additional features only for borderline cases. This approach achieves high precision while reducing average computational time by avoiding full feature analysis for all users
Data Source
AI summary
A means and system is designed to distinguish human users from bots (automated programs to generate posts or interactions) in social media (including microblogging services and social networking services) by assigning a likelihood score to each user for being a human or a bot. The bot score assigned to each user is computed from statistical, temporal and text features that are detected in user's social media interactions (relative indicators specific to a given social media data set) and user's historical profile information.