Group Message Review Using Context-Sentiment Similarity
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Solution Overview
Problem
Moderating incoming messages in group messaging services is challenging due to the difficulty in reviewing content that may be offensive, irrelevant, or contain malware, which can frustrate group members and overwhelm administrators.
Innovation Solution
Implementing a machine learning classifier to analyze the context and sentiments of a group messaging service, determining similarity scores between incoming messages and the group's context, and providing a message preview with sentiments to allow members to approve or reject messages before distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a group administrator manually reviews each incoming post before distribution, then the quality and relevance of group content is maintained, but the administrator becomes overwhelmed and lacks time to review posts carefully
Solution Approach 1:
The patent introduces an automated moderation system that acts as an intermediary between message senders and group administrators. This system includes a machine learning classifier that automatically analyzes incoming messages, determines their relevance to the group context, and filters appropriate messages before they reach administrators. The system uses natural language processing to understand message content and compare it against the group's established context, thereby maintaining content quality without requiring administrator time for each individual message review.
Solution Approach 2:
The patent implements a self-service moderation approach where the system automatically evaluates and filters messages based on predefined context parameters. The automated moderation system independently performs the review function by analyzing message content, determining relevance scores, and routing messages accordingly without requiring administrator intervention for routine messages. This allows the system to serve itself in the moderation task, freeing administrators from manual review burdens.
2Loss of time
If automated filtering is implemented to reduce administrator burden, then administrator time is preserved, but the system may lack the precision to accurately identify relevant versus irrelevant content
Solution Approach 1:
The patent employs parameter changes by utilizing multiple context parameters including topic keywords, sentiment analysis results, and relevance scores. The machine learning classifier dynamically adjusts its evaluation based on these parameters, comparing incoming messages against the group's contextual framework. By changing and weighing multiple parameters simultaneously, the system achieves high precision in determining message relevance without requiring administrator intervention.
Solution Approach 2:
The patent incorporates feedback mechanisms where the automated moderation system continuously learns from administrator decisions and group interactions. The system uses feedback loops to refine its classification accuracy by analyzing patterns in approved and rejected messages, adjusting its relevance determination algorithms accordingly. This feedback-driven approach enables the system to improve its precision over time while maintaining reduced administrator workload.
3Loss of information
If all incoming messages are displayed to group members, then information completeness is maintained, but members become frustrated by irrelevant or offensive content
Solution Approach 1:
The patent introduces an automated moderation system that acts as an intermediary between message senders and group members. This system includes a machine learning classifier that automatically analyzes incoming messages, determines their relevance to the group context, and filters appropriate messages before they reach administrators. The system uses natural language processing to understand message content and compare it against the group's established context, thereby maintaining content quality without requiring administrator time for each individual message review.
Solution Approach 2:
The patent extracts and removes harmful or irrelevant content from the message stream before it reaches group members. The automated moderation system identifies and filters out offensive, spam, or contextually inappropriate messages, extracting only the relevant and beneficial content for distribution to the group. This extraction process protects members from harmful factors while preserving the completeness of relevant information flow.
Data Source
AI summary
Methods, systems, and media for providing automated review of incoming messages in a group messaging service are provided. In some embodiments, the method includes: receiving, at a user device from a server, an incoming message to a particular message group in a group messaging service, wherein the user device is associated with the particular message group; determining, using a machine learning classifier at the user device, (i) a context of the particular message group based on a plurality of content items previously submitted by a plurality of users to the particular message group; (ii) a plurality of sentiments in the incoming message; (iii) a plurality of similarity scores, wherein each similarity score is a probability that a sentiment from the plurality of sentiments is similar to the context of the particular message group; and (iv) an aggregate similarity score based on the plurality of similarity scores is within a first range of predetermined values; and, in response to determining the aggregate similarity score is within the first range of predetermined values, displaying a preview of the incoming message on the user device, wherein the preview includes at least one sentiment from the plurality of sentiments.