Bestselling Books Project
Bestselling Books Project is an artistic data visualization that treats literary genre not as a fixed taxonomy but as a fluid social construct. Taking the Barthesian premise that to read is to struggle to name, it maps the networks of community-generated subject labels that readers actually use to describe bestselling books. By analyzing bestseller books data and user-generated book labels from Open Library's API, the project renders the relationships among the 200 most-used subject tags and the books they cluster, inviting viewers to explore genre boundaries that prove more contested than fixed.
Each book is sized by sales, and its brushstroke color and style encode its original language of publication.
While our dataset draws from open-source data, capturing neither every bestseller ever written nor sales details, this limitation aligns with our observation: literary classification, like any categorization system, is incomplete and ever-evolving.
Using JavaScript, p5.js brush, and an LLM to collapse approximately 80 raw labels into 11 navigable types, we created an exploratory interface that reveals unexpected connections between genres while preserving how readers actually describe and connect with books.
Daria Koshkina
Leading artist. Concept and art direction, data search, analysis, visualization and creative coding.
Daria Koshkina is on the Information Is Beautiful Awards 2024 Longlist. She is the 2025 Young Ones Jury and 2024 ONE Screen Jury. Her works were showcased at the London Design Festival, the Wrong Biennale and the CICA Museum. As a data visualization research specialist at Barabasi Lab, she contributed to projects exhibited at the Architectural Venice Biennale, the Ludwig Museum, the MEET Digital Culture Center, and the Postmasters Gallery. Her editorial and scientific illustrations were published by Vice, UN Women, Journal of Neuroscience, and Science. She has a B.A. in Illustration from the University of Hertfordshire and a Master of Digital Media and 3D Animation from Northeastern University. Through animation, digital painting, and data art, she explores how the reality of facts overlaps the subjective yet very real space of a personal experience.
Yixuan Liu
Data analysis, network science, visualization and creative coding.
Yixuan Liu is currently a Ph.D. student in network science at the Network Science Institute at Northeastern University, where she works in Barabási Lab. She studies large-scale complex systems using causal inference, network analysis, and LLMs. Her work develops interpretable, data-driven models that reveal hidden relational and causal patterns in computational social science.
Jose Gonzalez
Music compositing for the video format.
Jose Gonzalez is a pianist, composer, and educator. He holds a Bachelor of Music in Composition and Film Scoring from Berklee College of Music and a Master of Music in Composition from New England Conservatory. He is the creator of Groovetude, an online music education project focused on rhythmic concepts, music theory, and performances of original music. His work combines composition, education, and creative technology, with an interest in making complex ideas clear, engaging, and accessible.
David Y. Stephenson, Belal Haikal | Code review.
