Content Demotion System for Objectionable Material Detection
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Solution Overview
Problem
Social networks face challenges in filtering out objectionable content, such as false information, which degrades user experience and compromises the integrity of the platform, as conventional methods fail to effectively identify and demote such content before it proliferates.
Innovation Solution
A system and method that analyze content items for signals indicative of objectionable material, such as domain signals, keyword signals, and reporting signals, to determine a demotion value that adjusts the rank value of content items in a news feed, thereby downranking potentially objectionable content and reducing its visibility to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional content filtering methods are used, then the news feed can be generated, but objectionable content such as false information cannot be effectively identified and demoted
Solution Approach 1:
The content analysis system is segmented into multiple independent signal detectors, each specializing in identifying specific types of objectionable content (e.g., false information signals, spam signals, malicious content signals). This segmentation allows the system to maintain high reliability for different content types while keeping individual detector modules manageable in complexity.
Solution Approach 2:
The content analysis system is designed as a universal multi-functional platform that can detect various types of objectionable content through different signals. The same infrastructure handles false information, spam, malicious content, and other objectionable material, reducing overall system complexity while improving reliability across content types.
2Measurement precision
If multiple signals are analyzed to determine demotion value, then the accuracy of identifying objectionable content is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis by evaluating multiple signals simultaneously rather than sequentially. Signal values for false information, spam, malicious content, and other objectionable material are computed in parallel during content ingestion, reducing processing time while maintaining high detection accuracy through comprehensive signal analysis.
Solution Approach 2:
The system dynamically adjusts the weighting and threshold parameters of different signals based on content type, user profile, and contextual factors. This parameter optimization allows the system to achieve high detection accuracy while minimizing processing time by focusing computational resources on the most relevant signals for each content item.
3Object-affected harmful factors
If the rank value is adjusted based on demotion value, then the visibility of objectionable content is reduced, but the user experience may be affected by over-filtering legitimate content
Solution Approach 1:
The rank value adjustment is dynamic and adaptive rather than static. The system continuously monitors signal values and adjusts demotion levels in real-time based on content characteristics, user feedback, and evolving patterns of objectionable material. This dynamic approach reduces visibility of harmful content while adapting to legitimate content that may temporarily trigger false positive signals.
Solution Approach 2:
The system incorporates feedback mechanisms where user interactions with demoted content, reporting of false positives, and engagement metrics are used to refine future rank value adjustments. This feedback loop improves the system's ability to distinguish between genuinely objectionable content and legitimate content that may have triggered initial demotion signals.
Data Source
AI summary
Systems, methods, and non-transitory computer readable media configured to determine a value associated with at least one signal indicative of objectionable material in a content item. The value associated with the at least one signal indicative of objectionable material can be compared with a threshold value associated with the at least one signal. A demotion value can be determined in response to satisfaction of the threshold value associated with the at least one signal.