Aggregated Offensive Word Filters for Cultural Adaptability
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Solution Overview
Problem
Existing systems lack an effective method to identify and manage offensive language across diverse cultural and individual thresholds, leading to inconsistent content filtering and user experiences.
Innovation Solution
A computer-implemented system and method that receives and processes strings of words against aggregated offensive word filters, associating severity values with words and using offensiveness thresholds to determine and mitigate offensive content, allowing users to customize their filtering settings based on personal or regional standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single offensive word filter is used, then the system is simple to operate, but it cannot accommodate diverse cultural and individual standards for offensive language
Solution Approach 1:
The offensive word filter is segmented into multiple independent filter lists, each representing different cultural or individual standards. Each filter list contains words specific to its cultural context or sensitivity level, allowing the system to handle diverse standards independently while maintaining overall simplicity through modular architecture.
Solution Approach 2:
The filtering system is designed with multi-functionality to serve multiple cultural and individual standards simultaneously. A single unified filtering mechanism can select and apply appropriate filter lists based on user preferences, cultural context, or sensitivity settings, making the system universally adaptable without requiring separate systems for each standard.
2Adaptability or versatility
If multiple offensive word filters are aggregated, then the system can accommodate diverse cultural and individual standards, but it increases system complexity
Solution Approach 1:
The system implements self-service capabilities that automatically manage the aggregation and coordination of multiple offensive word filters. The system can automatically select appropriate filter lists based on detected context, user profiles, or cultural settings, eliminating the need for manual configuration and simplifying operation while maintaining adaptability to diverse standards.
Solution Approach 2:
An intermediary layer is introduced between the multiple filter lists and the content being filtered. This intermediary manages the aggregation logic, selects appropriate filters based on context, and coordinates their application, thereby hiding the complexity of managing multiple filters from the user and maintaining ease of operation.
3Measurement precision
If severity values and offensiveness thresholds are implemented, then the filtering becomes more precise, but it increases the complexity of determining offensive content
Solution Approach 1:
The system uses parameter changes by assigning severity values and offensiveness thresholds to words and filter lists. These numerical parameters enable precise control over filtering behavior, allowing the system to adjust sensitivity levels and prioritize different types of offensive content through simple numerical comparisons rather than complex logical evaluations.
Data Source
AI summary
Computer-implemented systems and methods are provided for identifying language that would be considered obscene or otherwise offensive to a user or proprietor of a system. A first plurality of offensive words are received, and a second plurality of offensive words are received. A string of words are received, where one or more detected offensive words are selected from the string of words that matches words from the first plurality of offensive words or the second plurality of offensive words. The string of words is processed based upon the detection of offensive words in the string of words.