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What were Americans reading during a pandemic, a financial crisis,
after an election? Do books serve as an escape from the present, a
source of information and education, or comfort? Do what Americans
search on the web reflect what books they read? And, how might we
foster an environment that encourages reading beyond the headlines?
By retrieving and synthesizing data from Google Trends, New York Times Best Seller Lists, and Google Books API, we created an interactive experience that visualizes correlations between books on the NYT Best Seller List and Google Trends based on topic and year.
Computational Design Thinking
with Kyuha Shim | Fall 2020
Our process of designing the relationship between current events and
books began with a decision on the sources of data that convey what
books people are reading and searching on the web. We created
datasets from Google’s yearly trends and the New York Times
bestseller lists of fiction and non-fiction books from 2006 to
2019 — from the earliest publication of yearly data from Google
trends in the U.S. to the latest publication of yearly data from NYT
bestseller lists. We retrieved and stored additional information
using Google Books API, including author names and book
descriptions. We created an additional layer of information by
generating classifying keywords from each book using
Twinword Text Classification API.
The integration required interpretations of data considering the multiple layers of interaction. The mapping of trends to books was based on time and relatedness: we created an array of related words for each trend and returned books in which the trend and related words appeared in the descriptions. The result fuses different sources of data to enable users to explore the content by time, then by keywords.