Trend Detection in Messaging Platforms Using Time Series Binning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods face challenges in accurately detecting trends in social data due to difficulties in distinguishing between real-world events and statistical fluctuations, with trade-offs in time-to-detection, precision, and recall, and the complexity of associating online social interactions with offline world impacts.
Innovation Solution
A computing device employs a trend detection model to analyze time series data of social interactions, using metrics such as velocity, acceleration, and nodality to identify trends by comparing data against background models, and outputs indications of detected trends, facilitating targeted content delivery based on demographic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used to detect trends in social data, then trend detection can be performed, but accuracy is reduced due to inability to distinguish real-world events from statistical fluctuations
Solution Approach 1:
The patent segments the trend detection process into multiple independent components: (1) receiving social data streams and extracting parameters, (2) binning data by time intervals and counting occurrences, (3) selecting appropriate trend detection models based on data characteristics, (4) calculating trend measures using selected models, and (5) comparing against thresholds. This segmentation allows each component to be optimized independently, improving overall accuracy while reducing false positives through systematic validation at each stage.
Solution Approach 2:
The patent employs parameter changes by selecting different trend detection models (e.g., linear regression, exponential smoothing, Poisson processes) based on the specific characteristics of the social data being analyzed. The system dynamically adjusts model parameters such as time bin sizes, trend thresholds, and detection windows to match the nature of the data stream, thereby improving measurement precision for different types of trends while maintaining reliability across diverse data types.
2Loss of time
If trend detection is performed rapidly, then time-to-detection is reduced, but precision decreases due to difficulty in distinguishing real events from fluctuations
Solution Approach 1:
The patent applies preliminary action by pre-processing social data streams into standardized formats with extracted parameters and time bins before trend detection begins. The system pre-selects from multiple trend detection models based on initial data characterization, and pre-establishes threshold criteria for each model type. This preliminary preparation enables rapid detection without sacrificing precision, as the heavy lifting of data transformation and model selection is completed before the actual trend analysis begins.
Solution Approach 2:
The patent implements dynamics by making the trend detection system adaptive and configurable based on real-time data characteristics. The system dynamically selects which trend detection model to apply based on the observed patterns in the social data, and can adjust detection parameters such as time window sizes and threshold values. This dynamic approach allows the system to maintain high precision across different types of trends while achieving rapid detection through optimized model selection rather than exhaustive analysis of all possible models.
3Measurement precision
If multiple trend detection models are used, then detection precision is improved, but device complexity increases
Solution Approach 1:
The patent applies local quality by matching specific trend detection models to specific types of social data patterns. Rather than applying a single complex model to all data or using multiple models simultaneously for every analysis, the system selects the most appropriate model based on local characteristics of the data stream. For example, linear regression models are applied to data showing linear growth patterns, while exponential models are used for viral spread patterns. This localized model selection improves precision for each data type while keeping the overall system complexity manageable through rule-based selection criteria.
Data Source
AI summary
A system and method for trend detection in a messaging platform. A trend detection model is selected and a time series having a plurality of instances of social data is received, wherein the instances of social data share a countable parameter. A count is made of occurrences of countable parameters in each instance of social data assigned to that bin and a trend detected based at least in part on the trend detection model and on the count for each bin.