Social Media ID Analysis for Fair Event Ticket Distribution
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Solution Overview
Problem
Automated ticket purchasing software utilizing APIs gains an unfair advantage over human ticket purchasers due to faster reaction times and higher volume of purchases, leading to prioritization issues in event ticket distribution.
Innovation Solution
An event ticket distribution system that utilizes social media identification information to calculate an index value for potential buyers, assigning priority flags based on account history, allowing loyal and dedicated fans to receive priority ticket purchasing opportunities while limiting automated resellers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated software is used for ticket purchasing, then purchasing speed and volume are improved, but fairness in ticket distribution deteriorates
Solution Approach 1:
The system performs preliminary actions by requiring users to pre-register and link social media accounts before ticket sales. Account history is retrieved and analyzed in advance to establish baseline behavior patterns. This preliminary characterization allows the system to differentiate between human and automated users before the actual ticket purchasing event occurs, preventing automated software from gaining unfair advantages during the purchase itself.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring purchasing behavior and comparing it against established account history patterns. When automated behavior is detected during ticket purchases, the system responds by adjusting queue priority or blocking access. This feedback loop ensures that fairness is maintained dynamically during the ticket distribution process.
2Measurement precision
If account history analysis is performed, then ability to identify loyal fans is improved, but system complexity increases
Solution Approach 1:
The system creates simplified copies or representations of complex social media account data by extracting only the most relevant behavioral indicators (purchasing patterns, account age, activity levels). Instead of analyzing entire social media histories, the system generates condensed profile summaries that capture essential loyalty signals while reducing processing complexity.
Solution Approach 2:
The system transforms complex qualitative account history data into quantifiable parameters with specific weightings. By converting behavioral patterns into numerical scores and thresholds, the system simplifies the decision-making process while maintaining accuracy in identifying loyal fans.
3Reliability
If priority flags are assigned based on social media ID, then loyal fans receive priority access, but automated resellers may exploit the system
Solution Approach 1:
The system applies preliminary anti-action by implementing behavior-based validation checks before assigning priority flags. Account history is analyzed in advance to establish legitimate user patterns, and these patterns serve as a baseline for detecting exploitation attempts. By setting up these defensive measures beforehand, the system prevents automated resellers from exploiting the priority system without needing to react to each individual attempt.
Data Source
AI summary
A method for identifying a simulated social media account history is provided. The method may include querying a social media identification information (“social media ID”) to determine whether the account history includes one or more parameters that indicate whether the social media ID is related to an automated entity or a human entity. The parameters may include at least one of less than a threshold number of friends on the account. The parameters may include more than a threshold frequency of historic ticket purchases per unit time. The parameters may also include disparate location of historic ticket purchases per unit time. The parameters may also include a historic record of less than a threshold reaction time to a plurality of ticket offers.