User-Specific Toxicity Prediction Models for Game Chat
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for managing toxic and offensive messages in multiplayer video games are inefficient, relying on human moderators who may miss or misinterpret messages, leading to negative player experiences and psychological harm, and are costly to maintain.
Innovation Solution
A computer-implemented method using machine learning to predict the toxicity of text-based messages by generating prediction models based on user feedback and context, allowing for real-time annotation or blocking of offensive content to prevent its display to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human moderators are used to manage toxic messages, then messages can be reviewed and filtered, but the cost of maintenance increases and moderators may miss or misinterpret messages
Solution Approach 1:
The patent replaces the mechanical system of human moderators with an automated machine learning-based prediction model. The system uses natural language processing algorithms to analyze chat messages, predict toxicity levels, and filter offensive content automatically. This substitution eliminates the need for human moderators while improving consistency and accuracy in identifying toxic messages through scalable automated processing.
2Device complexity
If automated filtering systems are implemented, then costs are reduced, but accuracy in identifying toxic messages may decrease
Solution Approach 1:
The system enables self-service through automated prediction models that continuously learn and adapt from user feedback. The machine learning algorithms automatically adjust their toxicity thresholds and classification criteria based on real-time data, eliminating the need for expensive human intervention while maintaining high accuracy. The system serves itself by automatically training, evaluating, and improving its own performance through integrated feedback loops.
Solution Approach 2:
The patent implements feedback mechanisms where user responses to predicted toxic messages are fed back into the prediction model to continuously improve accuracy. The system collects data on user reactions, adjusts toxicity thresholds based on actual user behavior patterns, and retrains models to reduce false positives and negatives. This closed-loop feedback system ensures continuous improvement of identification accuracy without additional human moderation costs.
3Reliability
If real-time toxicity prediction is performed for all messages, then player experience is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by selectively processing messages based on predicted toxicity probability. Instead of analyzing every message in full detail, the system first performs a quick initial assessment and only applies comprehensive analysis to messages that fall into ambiguous or potentially toxic ranges. This approach maintains high player experience quality by filtering obvious toxic content rapidly while investing more computational resources only where needed, thereby reducing overall processing time.
Data Source
AI summary
Using user-specific prediction models, it is possible to present an individualized view of messages generated by users playing a shared instance of a video game. Further, users with different subjective views of what is offensive may be presented with different forms or annotations of a message. By personalizing the views of messages generated by users, it is possible to reduce or eliminate the toxic environment that sometimes forms when players, who may be strangers to each other and may be located in disparate locations play a shared instance of a video game. Further, the user-specific prediction models may be adapted to filter or otherwise annotate other undesirable messages that may not be offensive, such as a message generated by one user in a video game that includes a solution to an in-game puzzle that another user may not desire to read as it may spoil the challenge for the user.