Display System Popularity Quantification via Big Data Analysis
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Solution Overview
Problem
Current methods for gauging digital content popularity on social media platforms are susceptible to manipulation and lack objective quantification, making it difficult for creators to accurately assess their content's resonance with audiences, especially for newcomers.
Innovation Solution
A team-based mechanism that combines digital content display contests with algorithmic analysis of big data to quantify social media popularity, using a display system with a display server and user big data server to record and calculate social media interaction behaviors and generate a popularity score based on liking, commenting, sharing, following, and browsing activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple summation metrics (likes, comments) are used to gauge digital content popularity, then the measurement process is simple and easy to implement, but the results are susceptible to manipulation and lack objectivity
Solution Approach 1:
The patent transforms the popularity measurement from simple count metrics to a multi-dimensional parameter system that includes interaction quality, user influence weight, and behavioral patterns. This changes the measurement parameters from superficial counts to weighted composite indicators that are harder to manipulate and more reflective of genuine popularity.
Solution Approach 2:
The patent creates a composite popularity metric by combining multiple data types (likes, comments, shares, user influence scores, interaction patterns) into a unified popularity index. This composite approach类似于composite materials, where the combination of different elements creates a more robust and manipulation-resistant measurement system than any single metric alone.
2Loss of information
If subjective human evaluations are used to judge digital content popularity, then some insight into content resonance is provided, but the results carry high subjectivity and lack quantifiable metrics
Solution Approach 1:
The patent replaces the mechanical system of human subjective judgment with an automated algorithmic evaluation system that processes social media interaction data. This substitution eliminates human subjectivity while maintaining the ability to detect content resonance through pattern recognition in user behaviors, producing quantifiable and objective popularity metrics.
Solution Approach 2:
The system implements feedback loops where user interactions continuously inform and refine the popularity calculation algorithms. This allows the system to learn from actual user behaviors and adjust its measurement criteria dynamically, maintaining sensitivity to content resonance while preserving objectivity through data-driven rather than human-driven judgments.
3Ease of manufacture
If individual creators publish digital content independently, then the publishing process is simple and direct, but accumulating engagement depends heavily on personal prominence which disadvantages newcomers
Solution Approach 1:
The patent merges individual creator efforts into collaborative display teams where multiple creators pool their resources, audiences, and engagement. This combining allows newcomers to benefit from the collective prominence of the team, accelerating engagement accumulation without compromising the simplicity of individual content creation and publishing.
Data Source
AI summary
A Method implemented by a display system for hosting digital content team display contests and quantifying social media popularity, is implemented through a display system. The system consists of a display server and a user big data server. The method involves multiple users participating in a display team, where the display server showcases the digital content contributed by these users as showpiece. Furthermore, the display server displays the showpiece and/or the display team in a display theme and records the social media interaction behavior data of the showpiece and/or the display team, which is then transmitted to the user big data server. The user big data server calculates the social media interaction behavior data of the showpiece and/or the team based on the quality differences in social media interaction behaviors and stores them separately in a database. Users can increase the popularity and value of their created digital content by participating in the display team.