Trend Detection for Content Targeting Using Information Distribution
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Solution Overview
Problem
Information distribution platforms struggle to detect and utilize trends in user-generated content effectively, leading to suboptimal user experiences and inefficient targeted content delivery.
Innovation Solution
An information distribution system that analyzes metrics such as velocity and acceleration of user-generated content with specific hashtags to determine trending scores, sending demographic data to content providers for targeted content delivery when scores meet a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If information distribution platforms allow users to share information in real-time with hashtags, then user engagement and content distribution speed improve, but the ability to detect and utilize trends effectively deteriorates
Solution Approach 1:
The system pre-calculates and stores metric values (velocity, acceleration, nodality) for hashtags as they emerge in the message stream. This preliminary computation allows the trend detection algorithm to quickly evaluate trending scores without real-time calculation delays, enabling both fast content distribution and accurate trend identification.
Solution Approach 2:
The patent introduces an intermediary trend detection layer between message reception and content distribution. This layer computes trending scores based on multiple metrics and acts as a mediator to identify trends accurately without slowing down the overall system, resolving the contradiction between speed and measurement precision.
2Measurement precision
If the system analyzes multiple metrics (velocity, acceleration, nodality) to determine trending scores, then trend detection accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the trend detection process into distinct computational components: velocity calculation (rate of hashtag usage), acceleration calculation (rate of change of velocity), and nodality calculation (network influence metrics). Each metric is computed independently and then integrated into the overall trending score, making the complex system manageable and maintainable.
3Adaptability or versatility
If the system sends demographic data to content providers for targeted content delivery, then content targeting effectiveness improves, but data processing time increases
Solution Approach 1:
The system pre-computes and stores demographic data associated with hashtags and user groups before trend detection is needed. When a trend is identified, the relevant demographic data is already prepared and can be quickly transmitted to content providers, eliminating data processing delays while maintaining effective content targeting.
Data Source
AI summary
In some examples, a method includes receiving, from one or more client devices, a stream of messages composed by one or more users of the one or more client devices, wherein each of the messages includes a particular hashtag, determining, using a set of metrics that are based at least in part on the messages, a trending score that represents a magnitude of a trend for the particular hashtag, in response to determining that the trending score satisfies a threshold, sending, to a content provider system, a set of demographic data that describes one or more of the users who associated with the particular hashtag, and, in response to receiving, from the content provider system, targeted content that is based at least in part on the particular hashtag and the set of demographic data, sending, for display at the one or more of the one or more client devices, the targeted content.