Context-Aware Abusive Language Detection Using ML
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Solution Overview
Problem
Existing methods for detecting abusive language in media lack context-awareness, leading to improper identification and response, particularly in cases where words or phrases can be used both abusively and non-abusively, and current systems fail to account for nuanced language usage.
Innovation Solution
A computer-based system employing machine learning models to analyze the context of abusive language in media, using multi-head attention networks and bidirectional gated recurrent units to determine the ambiguity of words or phrases, and applying active learning to refine the analysis, which considers grammatical context, location, and translations to accurately label sentences as offensive or non-offensive.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated keyword-matching techniques are used to detect abusive language, then detection speed and productivity are improved, but measurement precision and reliability deteriorate due to inability to distinguish contextual nuances
Solution Approach 1:
The patent introduces an intermediate contextual analysis layer between simple keyword matching and final abusive language classification. This intermediary step analyzes grammatical context, surrounding words, and semantic relationships to determine whether a matched keyword is actually used abusively, thereby resolving the contradiction between fast automated detection and accurate identification.
Solution Approach 2:
The patent replaces the mechanical keyword-matching system with an intelligent language analysis system that uses natural language processing, semantic analysis, and contextual understanding. This substitution maintains automated processing speed while dramatically improving identification accuracy by understanding the meaning and context of language usage.
2Measurement precision
If intensive manual evaluation is used to analyze abusive language context, then measurement precision is improved, but productivity and ease of operation worsen due to time-consuming processes
Solution Approach 1:
The patent implements a self-service automated system that performs contextual analysis, ambiguity detection, and abusive language classification without requiring manual human evaluation. The system uses machine learning models and natural language processing to autonomously analyze language context, achieving both high precision and high productivity simultaneously.
Solution Approach 2:
The patent performs preliminary automated contextual analysis and ambiguity detection before final classification, pre-processing the language data to identify cases that require detailed analysis. This preliminary action enables the system to efficiently handle most cases automatically while maintaining high accuracy, thereby improving overall productivity without sacrificing precision.
3Device complexity
If simple keyword filtering is used, then ease of manufacture and device complexity are reduced, but adaptability worsens due to inability to handle ambiguous language usage
Solution Approach 1:
The patent implements a dynamic analysis system that adapts its detection strategies based on the specific linguistic context encountered. The system can adjust its analysis depth, switch between different detection methods, and learn from new patterns, providing high adaptability for handling ambiguous language while maintaining reasonable system complexity through modular architecture.
Solution Approach 2:
The patent changes the parameters of language analysis from simple keyword presence to multi-dimensional contextual parameters including grammatical role, semantic meaning, surrounding context, and usage patterns. This parameter transformation enables the system to handle ambiguous language effectively while the modular implementation keeps the overall system complexity manageable.
Data Source
AI summary
Devices, systems, and methods are provided for context-based abusive language detection and responses. A method may include identifying text associated with first video content, and determining that a first word in the text matches a first keyword indicative of abusive language. The method may include determining a first label associated with the first word, the first label indicating that the first word is ambiguous. The method may include identifying a first sentence of the text, the first sentence including the first word. The method may include determining first and second context of the first word and the first sentence. The method may include determining, based on the first and second context, using a machine learning model, a second label associated with the first sentence, the second label indicating a probability that the first sentence includes abusive language. The method may include generating second video content for presentation.