Content Capital Calculation for Social Influence Measurement
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Solution Overview
Problem
Current content management and rating systems fail to accurately determine an individual's influence on others within their social network, as they do not provide information on how much a person has influenced their peers in introducing new content, and existing systems are not designed for people who know each other, leading to a lack of understanding of who is a trendsetter and how much they influence others.
Innovation Solution
A method and system that calculates 'content capital' scores based on the usage and sharing of media content within a social network, where users can determine their influence on others by tracking and aggregating data on media content sent, received, played, and forwarded, allowing for the visualization of influence levels between users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current rating systems are used to measure influence, then general influence scores can be obtained, but individual influence on specific friends and reciprocal influence cannot be determined
Solution Approach 1:
The patent segments the single general influence score into multiple individual influence scores, one for each friend in the social network. This allows the system to measure and display the specific influence a user has on each individual friend, rather than providing only an aggregate score that loses individual relationship information.
Solution Approach 2:
The patent implements reciprocal influence scoring where both users in a friendship pair receive influence scores from each other's perspectives. This feedback mechanism allows users to see not only their influence on others but also how much others influence them, creating a complete picture of mutual influence dynamics.
2Measurement precision
If explicit rating systems are implemented for content sharing, then influence measurement becomes possible, but user burden and system complexity increase significantly
Solution Approach 1:
The patent makes the social network platform itself perform the influence measurement work automatically by analyzing existing content sharing data and interaction patterns. Users do not need to manually rate or provide feedback about their influence; the system calculates influence scores autonomously based on observable behaviors like content sharing, forwarding, and engagement metrics.
Solution Approach 2:
The patent transforms influence measurement from a subjective rating task into an objective calculation based on quantifiable parameters such as content sharing frequency, forwarding behavior, and engagement metrics. This parameter-based approach automates the measurement process and eliminates the need for complex manual rating systems.
3Measurement precision
If content sharing tracking is implemented to measure influence, then influence scores can be calculated, but privacy concerns and data collection requirements arise
Solution Approach 1:
The patent extracts only the essential data elements needed for influence calculation from the vast amount of available user data. Instead of collecting and analyzing all user activities, the system focuses specifically on content sharing events, forwarding behaviors, and direct engagement metrics that directly relate to influence measurement, minimizing unnecessary data collection.
Data Source
AI summary
A server [115] or client-based content storage unit includes a communication device [300] to receive data corresponding to a transfer of at least one of media content and a link to the media content, from a first user [120] to a second user [125]. The communication device [300] also receives data corresponding to usage of the media content by the second user [125], and transmits some representation of a content capital to the first user [120]. A content usage aggregator [315] receives information corresponding to the usage of the media content by the second user [125]. A content usage evaluator [310] determines the content capital based on an aggregation of monitored use of the media content by the second user [125].