Cross-Platform Social Network Content Moderation System
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Solution Overview
Problem
Social networks face challenges in detecting and preventing the proliferation of bad content, particularly when it falls into unknown categories or formats, and struggle to identify passive-aggressive user behavior, as existing policies are often implemented in siloes and lack cross-platform support.
Innovation Solution
A social network data processing and monitoring system that employs AI models for content categorization and sentiment analysis, scoring messages and users based on content and sentiment, and utilizes a miscellaneous category to catch unknown bad content, while tracking user behavior across multiple platforms to flag potential bad actors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing content moderation policies are implemented in siloes on individual platforms, then each platform can maintain its own content standards, but the system fails to detect cross-platform bad actors and unknown bad content patterns
Solution Approach 1:
The patent combines multiple social network platform monitoring into a single centralized system that aggregates content and user behavior data across platforms. This merging enables cross-platform detection of bad actors and unknown bad content patterns while maintaining the ability to apply platform-specific moderation policies.
Solution Approach 2:
The monitoring system is designed with universal functionality to handle multiple social network platforms simultaneously. It can detect various types of bad content (known and unknown categories) and identify bad actors across different platforms using a unified approach, while still allowing for platform-specific policy implementation.
2Adaptability or versatility
If AI models are trained only on known bad content categories, then training data is easier to obtain, but the system cannot detect unknown bad content or passive-aggressive behaviors
Solution Approach 1:
The system performs preliminary analysis of content patterns and user behavior to identify potential unknown bad content categories before they become widespread. By monitoring emerging patterns and anomalies in real-time, the system can adapt to new types of bad content without requiring extensive training data for each specific category.
Solution Approach 2:
The monitoring system continuously learns from detected bad content patterns and user behavior feedback. When new types of bad content or passive-aggressive behaviors are identified, the system adjusts its detection algorithms and categorization approaches, enabling it to adapt to unknown content categories over time through iterative learning.
3Measurement precision
If the system monitors all user messages across multiple platforms, then comprehensive bad actor detection is achieved, but processing time and computational resources increase significantly
Solution Approach 1:
The system applies different levels of monitoring intensity to different users and content types based on risk assessment. High-risk users and content showing signs of bad behavior receive more intensive analysis, while low-risk content receives lighter monitoring. This localized quality approach maintains detection accuracy while reducing overall processing time.
Solution Approach 2:
The system performs full detailed analysis only when necessary (when suspicious patterns are detected), rather than analyzing every single message in depth. For routine content, lighter monitoring is applied, and full analysis is reserved for cases where anomalies or potential bad behavior are identified, thus balancing precision with processing efficiency.
Data Source
AI summary
A social network data processing and profiling system analyzes messages from a plurality of social networks to identify bad content and users posting bad content. The messages analyzed include primary posts and secondary messages which are posted in response to the primary posts. Keywords are initially obtained from the messages via applying content processing techniques. The keywords are used to categorize the messages into one or more of a plurality of categories. Sentiments are extracted from the messages. The messages are scored based at least on the contents and sentiments of the messages in addition to the actions represented by the messages if the messages are secondary messages. Messages with non-zero scores are aggregated to identify trends in the social networks and to identify users posting the messages.