Automated Content Removal System Using User Role Weighting
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Solution Overview
Problem
The proliferation of inappropriate content on the internet, such as abusive and illegal material, poses a challenge as existing methods rely on manual review processes that are time-consuming and inefficient, necessitating a solution for automatic removal.
Innovation Solution
A computer-implemented system that allows users to report inappropriate content, where the identity and role of the user, along with the content's category and reporting history, determine whether to automatically remove the content item, using a weighted scoring system to expedite the removal process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review process is used to remove inappropriate content, then accuracy and control over content removal is improved, but time consumption and processing efficiency deteriorate
Solution Approach 1:
The system enables automatic self-service by having the platform automatically review and remove inappropriate content based on machine learning models and predefined criteria, eliminating the need for manual administrator intervention and significantly reducing time consumption while maintaining reliable content removal through automated decision-making algorithms
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computer-based system that uses machine learning models, natural language processing, and predefined policies to automatically identify and remove inappropriate content, substituting human administrative actions with automated mechanical systems that operate continuously without time loss
2Productivity
If automatic removal process is implemented, then processing speed and efficiency are improved, but accuracy and control over content removal deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where users can report inappropriate content, and the system learns from these reports to improve its automated detection accuracy. The machine learning models continuously refine their performance based on feedback data, ensuring that automated removal decisions become more accurate over time while maintaining high processing speed
Solution Approach 2:
The patent employs dynamic parameter adjustments in the machine learning models to adapt to different content types and contexts. The system modifies detection thresholds and criteria based on evolving patterns in inappropriate content, allowing the automated system to maintain both high productivity and improving accuracy through continuous parameter optimization
3Reliability
If user identity and role are considered in removal decisions, then fairness and trustworthiness assessment is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary assessment of user identity and role before initiating content removal, using pre-established databases of user information and predefined policy rules. This preliminary action allows the system to quickly determine appropriate removal actions based on user trustworthiness and role-based criteria without adding significant complexity, as the assessment data is pre-collected and pre-organized
Solution Approach 2:
The patent implements a universal user profile system that serves multiple functions: it stores user identity information, assesses trustworthiness, determines appropriate removal actions, and applies role-based policies. This multi-functional approach consolidates what would otherwise be separate complex systems into a single integrated user management framework, improving fairness while controlling complexity
Data Source
AI summary
The disclosure generally describes computer-implemented methods, software, and systems for automatically removing inappropriate content. One example method includes: identifying a report of inappropriate content received from a user, the report identifying a content item the user has identified as inappropriate and an identification of the user, determining whether to automatically remove the content item based at least in part on the identity of the user, and removing the content item upon determining that the content should be removed. In some instances, the user is associated with a report weight. The report weight can be based, at least in part, on a business role of the user. Determining whether to automatically remove the content item may include determining that the user or a business role of the user is associated with an automatic removal rule, and removing the content item upon determining that the report is associated the user.