Social Signal Analysis for Content Strategy Optimization
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Solution Overview
Problem
There is a lack of tools that utilize social media optimization (SMO) and social signals to facilitate content strategy decisions for online publications, making it difficult for editors to determine what to write about, how many articles to publish, resource allocation, and which stories to promote effectively.
Innovation Solution
A processor-executed method and apparatus that evaluates content descriptors for online publications by gathering user signals, determining keyword relevance, and displaying this information in a graphical user interface, recommending topics to editors based on social signal counts and keyword associations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If editors rely on traditional content strategy methods without social signals, then content creation process is simple, but content effectiveness and audience engagement cannot be optimized
Solution Approach 1:
The patent introduces an intermediary tool that collects and processes social signals from multiple platforms (Facebook, Twitter, etc.) and presents them in an aggregated visual format. This intermediary layer translates complex social media data into actionable content descriptors, enabling editors to optimize content strategy without directly managing the complexity of individual social platforms
Solution Approach 2:
The patent replaces manual content strategy decision-making with an automated system that uses social signals as input data. The system automatically aggregates social signals, determines content descriptors, and generates visualizations, substituting the mechanical process of manual analysis with an automated information processing system
2Measurement precision
If editors manually analyze social media data for content strategy, then tool complexity is low, but time consumption and measurement precision are insufficient
Solution Approach 1:
The system performs preliminary actions by automatically collecting and aggregating social signals from multiple platforms before editors need to analyze them. The content descriptors are pre-computed based on social signal patterns, saving editors time and providing precise measurements that would be difficult to obtain through manual analysis
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring social signals and using them to generate content descriptors that guide future content creation. This feedback loop provides precise measurements of what content performs well socially, enabling data-driven decisions without manual time investment
3Adaptability or versatility
If no social signal tracking is implemented, then system complexity is low, but content strategy decision-making quality deteriorates
Solution Approach 1:
The patent creates a universal system that handles multiple social media platforms through a single interface. The content descriptor generation process is versatile, working across different content types and platforms, allowing editors to adapt content strategy based on aggregated social signals without needing separate tools for each platform
Solution Approach 2:
The system acts as an intermediary that translates diverse social signals from multiple platforms into a unified content descriptor framework. This intermediary layer provides adaptability by normalizing different social platform metrics into comparable content descriptors, enabling versatile content strategy decisions without direct integration complexity with each platform
Data Source
AI summary
Software at an online contributor website receives a list of websites having online publications. The software gathers counts of user signals for each online publication on each of the websites on the list. And the software determines content descriptors for each of the online publications. The software then counts the online publications at each website associated with each of the content descriptors and counts the user signals at each website associated with each content descriptor. The software displays the content descriptors for each website in a graphic in a graphical user interface, where the size of each content descriptor in the graphic reflects the count of online publications associated with the content descriptor and where the color of each content descriptor in the graphic reflects the count of user signals associated with the content descriptor.