Social Engagement Value Across Multiple Networks
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Solution Overview
Problem
Current methods lack an effective way to analyze and quantify social media engagement across multiple social networking services, hindering businesses' ability to understand customer sentiment and adjust their strategies accordingly.
Innovation Solution
A system and method that retrieve and analyze social media posts and associated statistical data from various social networking services to compute a social engagement value, which includes sentiment classification and tracking of re-publications, enabling businesses to adjust their activities based on this engagement value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple social networking services are monitored to provide comprehensive engagement analysis, then the completeness of social engagement value is improved, but the complexity of data collection and processing increases
Solution Approach 1:
The patent combines data collection from multiple social networking services (Facebook, Twitter, LinkedIn, Instagram) into a unified analysis system. The system merges disparate data sources including posts, comments, likes, shares, and sentiment data from different platforms into a single comprehensive social engagement value, resolving the contradiction by integrating multiple information streams while maintaining manageable system complexity through standardized processing pipelines.
Solution Approach 2:
The system implements a universal data processing framework that handles multiple types of social media data (text posts, images, videos, comments, likes, shares) from various platforms through a single analytical engine. This multi-functional approach allows the same system architecture to process diverse data types and sources, achieving comprehensive engagement analysis without proportionally increasing system complexity.
2Measurement precision
If sentiment classification and re-publication tracking are included in the analysis, then the precision of social engagement measurement is improved, but the computational resources required increase
Solution Approach 1:
The system performs preliminary sentiment classification and data categorization as data is collected from social networking services, before the main engagement value calculation. By pre-processing data to identify sentiment categories (positive, negative, neutral) and track re-publications in advance, the system reduces the computational burden during the final aggregation phase, achieving precise measurement while managing energy consumption through staged processing.
Solution Approach 2:
The analytical system segments the complex task of social engagement measurement into distinct modular components: data collection module, sentiment analysis module, re-publication tracking module, and engagement value calculation module. Each segment processes specific aspects of the data independently, allowing for optimized resource allocation and reducing overall computational requirements while maintaining high measurement precision through specialized processing for each function.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a non-transitory computer-readable medium, for obtaining a plurality of social media posts published by at least one social networking service, the social media posts including references to an entity, providing a social engagement value based on the plurality of social media posts, the social engagement value including a numeric value indicative of a level of social engagement of the entity with users in the at least one social networking service and selectively adjusting activities of the entity within the at least one social networking service based on the social engagement value.