Trending Entity Ranking via Social Engagement Scoring
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Solution Overview
Problem
Current methods for measuring social media impact are content-agnostic, failing to identify trending named entities within digital content objects, such as news stories, as they only track user engagement without analyzing the content itself.
Innovation Solution
A system and method that processes digital content objects using a computer system with an object scoring module to determine object scores based on social media activity metrics and a named entity recognition (NER) classifier to extract and rank named entities, calculating entity scores by aggregating object scores and sorting them to provide an entity ranking list.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If social media engagement tracking is used to measure impact, then user engagement can be registered and tracked, but the content of digital objects cannot be analyzed to identify trending entities
Solution Approach 1:
The patent combines social media engagement tracking with natural language processing and named entity recognition systems. The object scoring module integrates engagement metrics (shares, likes, comments) with content analysis of digital objects to identify and rank trending named entities, merging previously separate functions into a unified system that delivers both engagement measurement and content intelligence
Solution Approach 2:
The patent introduces an intermediary NLP processing layer between raw digital content and engagement metrics. This intermediary system extracts named entities from content and links them to engagement data, enabling the identification of which specific entities are trending based on their appearance in high-engagement digital objects without losing the connection between content and user response
2Measurement precision
If named entity recognition is applied to extract entities from digital content, then entity identification is achieved, but additional processing complexity is introduced
Solution Approach 1:
The patent segments the processing system into distinct functional modules: an object scoring module that handles engagement metrics, an NLP module that performs named entity recognition, and an entity scoring module that aggregates results. This segmentation allows each component to specialize in specific tasks, improving entity identification accuracy while managing complexity through modular design and clear separation of concerns
3Ease of operation
If entity ranking is based on frequency of mention, then simple ranking is achieved, but trending entities cannot be identified across multiple digital objects
Solution Approach 1:
The patent adds a new dimension to entity ranking by incorporating object-level engagement scores into the entity scoring process. Instead of ranking entities solely by frequency of mention across digital objects, the system weights entity mentions by the engagement performance of the digital objects they appear in. This dimensional expansion transforms simple frequency counting into a sophisticated trend identification metric that captures both prevalence and popularity
Data Source
AI summary
A method and a system for natural language processing of digital content objects, such as news stories, which ranks named entities in digital content objects by the impact that digital content objects that mention them are having on social media, is provided. Digital content objects are scored on a per object basis based on social media activity metrics associated with that digital content object. Named entities that appear in each digital content object are also extracted through natural language analysis. The named entities are then scored on a per entity basis to obtain an entity score that the object scores of those digital content objects in which that named entity appears. An entity ranking list can be created based on the entity scores, which can then be used in various different ways. For example, the entity ranking list can be displayed on a graphical user interface.