Audience Influence Detection via Automated Interaction Analysis
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Solution Overview
Problem
Manually tracking user interactions with videos on a video sharing platform is time-consuming and laborious, making it impractical for administrators to effectively learn about their video audience.
Innovation Solution
A system and process that identifies a set of videos based on criteria, queries video and user databases to retrieve engagements and user data, determines influence scores, and sends alerts for influential audience members, enabling efficient audience analysis and interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If an administrator manually tracks user interactions with videos, then the administrator can learn about the video audience, but the process becomes very time-consuming and laborious
Solution Approach 1:
The patent introduces an automated system that acts as an intermediary between video users and administrators. This system automatically collects user interaction data, processes it through algorithms to identify influential audience members, and delivers insights to administrators. The intermediary handles the time-consuming tracking and analysis tasks, freeing administrators from manual effort while ensuring comprehensive audience information is captured and analyzed.
2Loss of information
If the administrator manually tracks numerous users' interactions, then complete audience data can be collected, but the task becomes impractical due to the large number of users
Solution Approach 1:
The system enables self-service automation where the automated tracking system independently collects, processes, and analyzes interaction data from numerous users without requiring administrator intervention. The system serves itself by automatically managing the entire workflow from data collection to generating insights about influential audience members, making the process practical even with large user bases.
Solution Approach 2:
An automated intermediary system handles the complex task of tracking numerous users by systematically collecting interaction data, processing it through analysis algorithms, and presenting consolidated insights. This intermediary manages the complexity of dealing with many users, transforming raw interaction data into manageable information about influential audience members.
3Measurement precision
If the system analyzes all user engagements to identify influential audience members, then accurate identification is achieved, but the processing complexity increases
Solution Approach 1:
The system applies local quality by focusing analysis on specific characteristics that define influential audience members rather than treating all users uniformly. It identifies and weights particular engagement patterns, interaction types, and user behaviors that are locally significant for determining influence. This targeted approach maintains identification accuracy while reducing overall processing complexity by concentrating computational resources on relevant features.
Solution Approach 2:
The system manages complexity through parameter changes by dynamically adjusting analysis parameters such as influence thresholds, engagement weightings, and time windows. It modifies these parameters based on data characteristics and processing requirements, allowing accurate identification of influential members while adapting the processing complexity to match the specific dataset and analytical needs.
Data Source
AI summary
Determining a video audience is disclosed, including: identifying a set of videos based at least in part on a received criterion; querying a video database to retrieve engagements associated with each of at least a subset of the set of videos; identifying a set of audience members associated with the engagements associated with each of the at least subset of the set of videos; and querying a user database to gather events associated with each of at least a subset of the set of audience members.