Please join us for a UW Data Science Seminar featuring Neel Gupta and Shirin Khanam on Tuesday, October 21st from 4:30 to 5:20 p.m. PT. The seminar will be held in IEB G109.

“Using the TikTok Research API to Investigate the BookTok Phenomenon”

Abstract: “BookTok,” a large bookish subcommunity on TikTok, has emerged as a surprisingly influential force in the field of contemporary literature over the last several years. When books are discussed on TikTok, their sales often skyrocket. Our research leverages the messy and often inconsistent TikTok Research API to characterize the growth of the BookTok community from 2020-2024. We also identify major genres, authors, and books that have gained particular prominence on the platform and have remained large reference points for the community. Both our data collection and analysis is in progress, and we welcome suggestions for further analysis.

Biography: Neel Gupta is a PhD student in the iSchool at UW working in the fields of cultural analytics and digital humanities. He received his undergraduate degree from Swarthmore College in English and Mathematics. He’s interested in how economic, sociological, and technological changes in society have affected the cultural outputs of society, specifically in the realm of 20th and 21st century fiction. Neel is more broadly interested in how digital and computational methods can be used alongside humanistic methodologies to study culture. His recent research on Seattle Public Library Data has been published or is forthcoming in the Journal of Open Humanities Data and the Computational Humanities Research Conference.

“A web-based LumpIt”

Abstract: Rare genetic disorders affect 263-446 million persons or ~3.5–5.9% of the worldwide population, and the vast majority of these persons have a Mendelian condition (MC). Over 4,500 genes underlie one or more of the 6,000 MCs described to date, and ~25% of these genes underlie two or more MCs. However, there is actually no quantitative method for distinguishing between MCs due to variants in the same gene. Instead, researchers and clinicians define Mendelian conditions manually and subjectively based on an arbitrary selection of perceived shared clinical features. Conditions can be retroactively merged or separated through a process called “lumping and splitting.” This means we have no idea how many different rare diseases really exist. More importantly, the lack of objective approaches for determining when two claimed disease entities are sufficiently distinct to constitute two separate diseases limits the accuracy of information (e.g., natural history, anticipatory guidance, etc.) that clinicians provide to families with a likely pathogenic or pathogenic variant in one of these genes. We developed a machine learning tool, LumpIt, that can predict expert lumping and splitting decisions. Adoption of LumpIt by clinicians and researchers in rare disease will improve the precision of clinical diagnosis with MCs and accelerate discovery and delineation of new MCs.