Multi-Level Chat Classification for Moderator Workload Reduction
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Solution Overview
Problem
Current online chat filtering technologies, such as Bayesian filters, are inefficient and costly due to high false positives, overwhelming moderators with benign and unacceptable chat messages, requiring extensive manual intervention to distinguish between acceptable and unacceptable content.
Innovation Solution
A multilevel chat detection and classification system using Bayesian classifiers to label individual chat messages as acceptable or unacceptable, with a second classifier determining whether to escalate chat sessions to a moderator based on a ratio of unacceptable to acceptable messages or other triggering conditions, reducing unnecessary moderator reviews.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a Bayesian filter is used to monitor chat messages, then the filtering process is easy to predict and manage, but the accuracy decreases and false positives increase, overwhelming moderators
Solution Approach 1:
The chat monitoring system is divided into multiple independent classifiers, each specializing in detecting specific types of inappropriate content. Instead of using a single general-purpose Bayesian filter, the system segments the filtering task into multiple specialized components that work together, with each classifier focusing on particular patterns or categories of unacceptable chat.
Solution Approach 2:
The system dynamically adjusts the sensitivity and threshold parameters of individual classifiers based on their performance and the specific chat context. By changing parameters such as probability thresholds and weighting factors for different token patterns, the system optimizes the balance between false positives and false negatives for each classifier.
2Device complexity
If a single classifier monitors all chat messages, then the system structure is simple, but the productivity decreases due to high moderator workload
Solution Approach 1:
The monitoring system is segmented into multiple specialized classifiers, each handling specific types of content analysis. This segmentation allows parallel processing of different chat message aspects, reducing the overall workload on moderators by pre-filtering and categorizing messages before they reach human reviewers.
Solution Approach 2:
The system applies multiple classifiers to chat messages, performing more analysis than a single classifier would provide. This excessive action ensures thorough screening of potentially problematic content, allowing moderators to focus only on messages that require human judgment after automated analysis.
3Reliability
If conservative filtering is applied to catch all potential violations, then the reliability of detecting unacceptable content increases, but the loss of time increases due to moderator intervention requirements
Solution Approach 1:
The system applies multiple layers of classification to thoroughly analyze chat content, performing excessive filtering actions to ensure high reliability in detecting unacceptable content. Each classifier adds another layer of scrutiny, reducing false negatives while the coordinated system manages false positives through probabilistic analysis.
Solution Approach 2:
The system incorporates feedback mechanisms where classifier results inform subsequent classification decisions. Moderators' reviews of escalated messages provide feedback that refines classifier thresholds and parameters over time, improving reliability while reducing unnecessary moderator interventions as the system learns from accumulated data.
Data Source
AI summary
Embodiments presented herein provide systems, methods and articles of manufacture for a computer-implemented method to monitor users interacting in an online multiuser environment. For each message sent by a user during a session, determining, via a first classifier, whether the message is either acceptable or unacceptable for the online multiuser environment. Upon satisfying a triggering condition for the session of the user, determining, via a second classifier, whether to escalate the session to a moderator for review.