Safe and Sustainable Fleet Management with Data Analytics and Reinforcement Training; TikTok: Impact of Sociotechnical Relationship Between Data Collection and Personal Privacy on User Interaction with Social Media

Data collection has existed since the 1960s, but the widespread awareness of it is a recent trend starting around 2005 the rise of social media. Stories such as Facebook being sued over the Cambridge Analytica data breach have shed light on just how much of our daily life is touched by ‘big data’ (Criddle 2021). While the ethical nature of data collection is heavily debated, the way society changes due to data collection and personal privacy is relatively unknown. This portfolio explores how data collection affects society through two different methods. The technical project proposal utilizes the idea that drivers will improve their eco-driving when presented with their performance that is tracked using data collection on their driving methods. On the other hand, the STS research topic focuses on how the combination of data collection methods and personal privacy affect a user’s experience with social media, specifically TikTok. Both papers analyze how the power of data collection methods can affect the action or experience of a user. The technical project deliverables - new supplemental training materials, scorecards, and a timeframe of when to administer training - will help Facilities Management (FM) at University of Virginia improve safety and sustainability. The STS research paper deliverable will answer the research question: how are the sociotechnical relationships between data collection and personal privacy influencing user experience with social media, specifically with TikTok? Overall, the STS research paper will explain how data collection affects users, and the technical paper will use data collection methods to help fleet drivers.

The University of Virginia’s Facilities Management Fleet consists of around 260 total vehicles and is committed to safe and sustainable driving. Telematic tracking systems in FM vehicles provide feedback on a multitude of driving behavioral measures, including speeding, harsh braking, hard acceleration, seatbelt usage, harsh cornering, and idling time. Last year, data collected on these measures was used to develop relevant educational materials on mindful driving. Our team aimed to further improve safe and eco-friendly FM driving behaviors by analyzing how long fleet shops maintain mindful driving behaviors following their initial training, and if reinforcement training (additional scorecards and manager conversations) proves to be effective when given proactively or reactively to increased violations of driving behavioral measures. This paper outlines the process we used in finding how long training effects last based on statistical analysis on the various behavioral measures in the weeks following the first training. After determining when a significant decrease in these metrics occurred for various FM shops, we were able to decide the most effective time frame to administer the proactive reinforcement training program to certain FM shops. A separate group of shops received the reactive training after any significant increase in driver incidents was detected. These new reinforcement training programs were largely based on the professional FM driver education modules and provided conversation templates for managers to use in order to re-educate their shop’s respective drivers.

TikTok, owned by the Chinese company ByteDance, was released worldwide in August 2018, and their algorithm has changed the face of data collection in social media. Data collection is still a relatively new technology, heavily impacting almost all aspects of everyday life. Social media companies, like TikTok, collect data about users and “analyze it in an effort to draw conclusions about the populations of these users” (McCourt 2018). Stories, such as Facebook being used over the Cambridge Analytica data breach (Criddle 2021), showcase one of the larger concerns regarding social media’s data collection methods: the relationship between data collection and personal privacy. While the ethics of privacy and/or safety of users when analyzing user data is heavily debated, the effect of data collection and personal privacy on users' interaction with social media is not well known. This paper focuses on understanding how the sociotechnical relationships between data collection and personal privacy influence user experience with social media, specifically with TikTok. This research is analyzed using technological momentum to explain the evolving relationship between the technology, data collection methods, and society, users. To ensure that future technology will improve human life, it is important to fully understand the relationship. For many people, everyday life includes social media, so understanding the relationship between data collection and social media user’s experience can lead to great outcomes for data engineers, the STS field, and the public.

The two projects within this portfolio investigate different aspects of data collection and its effects. The technical paper utilizes data collection, and the analysis of it, to try and better society by improving the safety and sustainability of drivers. The STS research paper highlights the relationship between data collection and personal privacy and its effects on user experience. Completing these two papers simultaneously offered two highly different perspectives on data collection and analyzing practices that showcase negative and positive effects on society in everyday life. Most modern technology will have complex effects on society depending on the situation in which the technology is used. It is important to understand all these effects, so technology can best serve society. Therefore, researching data collection, a highly controversial technology at the moment, in multiple situations and lenses as done in this portfolio broadens the understanding of it for the STS field and society.

School of Engineering and Applied Science
Bachelor of Science in Systems Engineering
Technical Advisor: B. Brian Park
STS Advisor: Bryn Seabrook
Technical Team Members: Ryan Ahmadiyar, Jenny Chun, Caroline Fuccella, Damir Hrnjez, Benjamin Weisel