By Joseph Zhang | www.josephz.me

*Reading Trends is an interactive experience that visualizes correlations between NYT Best Seller List books and Google Trends, from 2006 to 2019, based on topic and year. We retrieved and synthesized data from Google Trends, New York Times Best Seller Lists, and Google Books API, and generated and comparing classifying keywords for each book using Twinword Text Classification API.*

https://s3-us-west-2.amazonaws.com/secure.notion-static.com/4e6be392-e1df-4a52-b5a5-1e7795232b0f/cover-image.jpg

Advisor: Kyuha Shim, Computational Design Thinking (Fall '21)

Project Duration: 4 weeks

Tools: HTML/CSS/JS, Google Books API, NYT Best Seller API, Twinword Test Classification API

Collaborators: Taery Kim, Alice Fang


My Role I handled all technical development of the project. This included designing and implementing the UX logic ss well as synthesizing over 6000 lines of JSON data. I also play the core part in concept development and data visualization.

Problem Space

Books not only serve as a source for learning but can also serve as an escape from reality. Can major events such as a pandemic, a financial crisis, or an election influence the types of content we choose to consume though?

Through this project, we wanted to explore if what Americans search on the web can reflect in the types of books they read. Curious to find out, we designed a web experience visualizing the potential relationships.

By retrieving and synthesizing data from Google Trends, NYT Best Sellers Lists, and Google Books API, and generating and comparing classifying keywords for each book using Twinword Text Classification 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.

Final Demo

https://vimeo.com/579207230