Entity Profile Data Accuracy via Affiliated User Verification
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Solution Overview
Problem
Auto-created profile pages on social networking sites often lack accurate information due to being susceptible to errors and abandonment, leading to incomplete or outdated data, which can be improved by crowdsourcing data from a subset of users affiliated with the entity.
Innovation Solution
A method and system that identifies entities with incomplete or abandoned profile pages, solicits users associated with the entity, scores potential candidates based on their affiliation and engagement, and prompts them to verify or provide data, ensuring accuracy through verification processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If auto-created profile pages are used for entities, then the profile pages can be created automatically without manual intervention, but the information accuracy and completeness deteriorates due to errors and abandonment
Solution Approach 1:
The system enables affiliated users to voluntarily verify and update entity profile information through automated notifications and prompts. Users self-initiate the verification process by receiving notifications about incomplete or inaccurate profile data, then independently contribute corrections without requiring manual intervention from the system administrator.
Solution Approach 2:
The system implements a feedback loop where profile completeness and accuracy are continuously monitored, and affiliated users are notified when updates are needed. The system provides feedback about specific missing or inaccurate information, tracks verification status, and maintains a history of profile updates to ensure ongoing reliability.
2Quantity of substance
If a generally undefined group of users is used for crowdsourcing, then a large volume of data can be collected, but the information accuracy deteriorates due to lack of affiliation incentive
Solution Approach 1:
The system applies different selection criteria to different user groups by specifically targeting users with demonstrated affiliation to the entity. Rather than uniformly selecting from all users, the system identifies and engages a specialized subset of users who have local knowledge and vested interest in the entity's accurate representation, thereby improving information quality.
Solution Approach 2:
The system changes the selection parameter from general user population to affiliated user population by using affiliation data, connection graphs, and user profile information to identify and invite only those users with demonstrated connection to the entity. This parameter change ensures both sufficient data volume and high accuracy.
Data Source
AI summary
Generally discussed herein are methods, systems, and apparatuses for crowdsourcing data. A method can include identifying a first entity has an auto-created profile on a social network site or has a profile page that has been abandoned by an administrator of the profile page, identifying a user of the site includes the first entity in their profile or includes a second entity in their profile where the second entity includes an industry identifier that matches an industry identifier of the first entity, prompting the user determined to be associated with the first entity to verify data about the first entity in the auto-created profile or provide new data about the first entity, and updating the auto-created profile of the first entity in response to the user confirming that the prior data is incorrect or providing new data about the first entity.