Subregion Characterization via Tag Distribution Analysis
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Solution Overview
Problem
Social networking systems lack the ability to accurately characterize and differentiate between subregions within larger geographic areas based on user-generated data, leading to ineffective targeting of advertisements and user experiences.
Innovation Solution
An online social networking system aggregates and analyzes user postings with location data and hashtags to generate a characterization of subregions by identifying common yet region-specific tags, using semantic analysis and topic modeling to create descriptive profiles of neighborhoods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the social networking system uses generic geographic targeting, then it can reach a broad audience, but it cannot accurately differentiate between subregions with distinct characteristics
Solution Approach 1:
The patent segments the geographic area into subregions and further segments the analysis by creating separate tag distributions at different geographic levels (region-level and subregion-level). This segmentation allows the system to identify tags that are specific to subregions while filtering out universally common tags, thereby improving subregion characterization accuracy without requiring a complete redesign of the entire system.
Solution Approach 2:
The patent introduces an additional dimension of analysis by comparing tag distributions across two geographic levels (region and subregion). Instead of analyzing only subregion-level tags, the system creates a two-dimensional analysis framework that identifies tags with high subregion specificity by finding tags common within subregions but rare at the region level. This dimensional approach enables precise subregion differentiation using existing data infrastructure.
2Measurement precision
If the system analyzes all user postings to characterize subregions, then it achieves comprehensive coverage, but it increases processing time and computational resources
Solution Approach 1:
The patent extracts only the necessary information from user postings - specifically location data and hashtags - rather than analyzing the entire content of all postings. By focusing extraction on these two key elements and comparing their distributions across geographic levels, the system achieves comprehensive subregion characterization while minimizing processing requirements. The method extracts tag frequency patterns rather than processing full text content.
Solution Approach 2:
The patent applies partial action by analyzing only the distribution patterns of tags rather than processing every detail of user postings. The system performs sufficient analysis to identify subregion-specific tag distributions but avoids excessive processing by not analyzing every posting in depth. This partial approach achieves the necessary characterization accuracy without the computational burden of complete analysis.
3Measurement precision
If the system uses detailed subregion tagging, then it improves advertisement targeting precision, but it reduces the quantity of usable data for analysis
Solution Approach 1:
The patent merges data from multiple sources and levels by combining region-level tag distributions with subregion-level tag distributions. Instead of relying on a single data source, the system integrates information from both geographic levels, creating a more robust dataset. This merging increases the effective data volume available for analysis while maintaining the precision needed for detailed advertisement targeting.
Solution Approach 2:
The patent creates a multi-functional tag distribution analysis system that serves multiple purposes: characterizing subregions, identifying neighborhood types, and enabling advertisement targeting. The same tag distribution data structure is used for multiple analytical functions, maximizing the utility of the available data. This universal approach ensures that the limited data is used efficiently across different applications without requiring separate data collection for each function.
Data Source
AI summary
In one embodiment, a method includes a computing device receiving postings from users of an online social networking system. A postings may include location data along with one or more tags that may describe the content of the posting. The computing device may identify regions and subregions from which the postings originated, and may determine a distribution of the tags according to two data dimensions: the ubiquity of the tags across the regions, and the ubiquity of the tags across the subregions. Based on the distribution, the computing device may create a neighborhood characterization to accurately describe one or more subregions. The computing device may also determine applications for the neighborhood characterization.