Detecting Scalper Bots: Machine Learning Solutions for Irregular Ticket Sales Detection; Analogies of Obsolescence: Narratives Driving Smartphone Replacement and E-Waste

Sociotechnical Synthesis
(Executive Summary)
Exploiting Scarcity in Concert Tickets and Smartphones

"The greatest threat to our planet is the belief that someone else will save it." – Robert Swan

Smartphones are replaced every two to three years on average, while e-waste piles up faster than we can recycle it. My STS research focuses on how consumers drive the smartphone obsolescence cycle, highlighting the interplay between social pressures, marketing strategies, and consumer behaviors. My technical report focuses on detecting scalper bots in online ticketing systems, using machine learning to ensure fairness and accessibility in concert ticket sales. While scalper bots manipulate digital systems for financial gain, consumer behaviors driving smartphone obsolescence reveal a different kind of exploitation: one driven by social pressures and corporate marketing. Both projects illuminate how human behavior interacts with product scarcity, demonstrating how insights from consumer behavior in one domain, such as smartphone obsolescence, can inform strategies to design systems that mitigate exploitation in another, such as ticketing platforms
The technical portion of my thesis produced a machine learning model aimed at detecting scalper bots in online concert ticket sales. Using datasets of past ticket sales per venue and graph-based techniques, the model identifies anomalous patterns such as rapid purchases and bulk transactions that are indicative of bot activity. Without access to the Ticketmaster API, I developed an alternative data processing framework to track suspicious behavior and scalped tickets across resale platforms like StubHub and SeatGeek. The significance of this work lies in its potential to improve ticket accessibility for genuine fans by mitigating the inequities caused by bots. By empowering ticketing platforms to foster fairness and trust, this project addresses a significant issue in the entertainment industry, where artificial scarcity distorts markets and alienates consumers.
In my STS research, I investigated how consumers drive the smartphone obsolescence cycle. Applying frameworks like Lahlou’s Installation Theory and concepts from Schwarz-Plaschg’s work on analogies, I explored how analogies from the physical, social, and mental spaces – the light bulb, drug pushing, and fast fashion – contribute to frequent device replacements. I analyzed how consumers often perceive smartphones not just as tools but as symbols of status and personal achievement, making them susceptible to marketing that emphasizes novelty over functionality. The significance of this research lies in its potential to reshape societal attitudes toward technology ownership. By emphasizing sustainable consumption, this work highlights opportunities for reducing e-waste. Addressing consumer-driven behaviors could help counteract the environmental impact of planned obsolescence and promote more sustainable practices in technology production and use.
Reflecting on these projects, I see them as two sides of the same coin, both revealing how technology mediates human behavior and how human actions, in turn, shape technological systems. The technical project is meaningful to me because it tackles a tangible frustration I have experienced firsthand with GPU scarcity: watching enthusiasts lose access to a product due to bot-driven scalping. By creating a machine learning model that detects and mitigates scalper activity, I felt empowered to address a problem that directly affects real people. This project reminded me of the importance of designing tools that not only solve technical problems but also protect the integrity of human experiences. My STS research, however, resonated with me on a broader, systemic level. Studying how consumers drive the smartphone obsolescence cycle revealed the hidden complexities behind everyday decisions, like upgrading to a new device. Drawing from readings like Pacey and Martin and Schinzinger, I was struck by how societal norms and marketing campaigns subtly frame what we perceive as “necessary” or “new.” This research challenged me to rethink my own relationship with technology and strengthened my belief that engineers must engage with the broader social contexts of their innovations. As I worked on my prospectus and presentations for STS 4600, I often reflected on the most important actors in these systems. For the technical project, it was the scalper bots and the genuine fans they exploit; for my STS research, it was the consumers, tech companies, and the e-waste they unwittingly create. Both projects revealed how intangible forces, like trust, fairness, and societal pressures, interact with tangible actors, such as algorithms or smartphones, to shape outcomes that affect people’s lives in profound ways. These realizations have deepened my understanding of ethical engineering, where solutions require not only technical precision but also a careful examination of the human systems they serve.
I would like to thank my advisors and mentors for their guidance throughout these projects. Special thanks to Red Light Management, a Charlottesville-based music management company that commissioned the model, for their valuable collaboration and insights into the entertainment industry. I am also grateful to Professor Kathryn Neeley for her thought-provoking discussions on ethics and engineering, which shaped my research approach.

School of Engineering and Applied Science

Bachelor of Science in Computer Science

Technical Advisor: Rosanne Vrugtman

STS Advisor: Kathryn Neeley