Entity Reputation Scoring for Media Content Filtering
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Solution Overview
Problem
Existing systems face challenges in efficiently filtering user-generated media content for offensiveness, as they rely on resource-intensive human review and are prone to false negatives, leading to potential damage to the hosting organization's reputation and user confidence.
Innovation Solution
Implementing a reputation service node that uses supervised or unsupervised machine learning algorithms to generate entity reputation scores based on diverse feature data, including community encounters and feedback, to proactively filter media content and identify trusted entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all user-submitted content is reviewed by the hosting organization, then content appropriateness is improved, but the amount of content that can be published is limited due to resource constraints
Solution Approach 1:
The patent introduces automated filtering systems and reputation scoring mechanisms as intermediaries between user-submitted content and the hosting organization's review process. These intermediaries pre-screen content, assign reputation scores based on multiple features, and prioritize reviews for human reviewers, thereby maintaining content appropriateness while enabling higher content volume to be processed
Solution Approach 2:
The patent replaces the purely mechanical human review system with an automated electronic filtering and scoring system. Machine learning algorithms analyze content features, user reputation, and community feedback to automatically filter inappropriate content and prioritize review queues, substituting manual review mechanisms with automated computational processes that can handle larger content volumes
2Reliability
If the content review bar is set high to ensure quality, then content appropriateness is improved, but user-provided content from active contributors is unnecessarily limited
Solution Approach 1:
The patent applies different review thresholds and filtering strictness levels based on local characteristics of content and contributors. Active contributors with established reputations receive more lenient filtering and lower review thresholds, while new or low-reputation users have their content subject to stricter scrutiny. This localized quality approach maintains high content standards overall while providing flexibility for trusted contributors
Solution Approach 2:
The patent implements dynamic review thresholds that adjust based on contributor reputation, content type, and community feedback. The system continuously adapts the stringency of content review based on the entity's established trust level, allowing the content acceptance criteria to be flexible rather than static, thereby maintaining quality while accommodating reliable contributors
3Reliability
If manual review processes are used to filter offensive content, then content safety is improved, but the system is prone to false negatives and resource-intensive operations
Solution Approach 1:
The patent implements preliminary automated filtering and reputation scoring before content reaches human reviewers. The system pre-processes content by analyzing features, checking against known offensive patterns, and evaluating contributor reputation in advance, thereby eliminating obviously inappropriate content before it consumes human review resources and reducing false negatives through multiple pre-screening layers
Data Source
AI summary
Techniques to filter media content based on entity reputation are described. An apparatus may comprise a reputation subsystem operative to manage an entity reputation score for an entity. The reputation subsystem comprising a reputation manager component and a reputation input/output (I/O) component. The reputation manager component may comprise, among other elements, a data collection module operative to collect reputation information for an entity from a selected set of multiple reputation sources. The reputation manager component may also comprise a feature manager module communicatively coupled to the data collection module, the feature manager module operative to extract a selected set of reputation features from the reputation information. The reputation manager component may further comprise a reputation scoring module communicatively coupled to the feature manager module, the reputation scoring module operative to generate an entity reputation score based on the reputation features using a supervised or unsupervised machine learning algorithm. Other embodiments are described and claimed.