Bitter Data: An Exploration into Data Edibilization of Negative Emotion
Li, Yufan; Huang, Yue; Zhang, Kang and Varvara Guljajeva (2023)
Abstract
“Bitter Data” transforms 100,000 distress postings from Chinese social media into a multi-sensory experience using data edibilization. We’ve mapped distress data quantity to the bitterness and color of tea through data analysis and experimentation. Participants taste, smell, and observe 11 cups of tea, each embodying a year’s distress data, in our workshop. Their facial expressions, recorded upon tasting, visually indicate emotional states. This project explores benefits and pragmatic solutions to challenges of data edibilization.
Venue: 2023 IEEE VIS Arts Program (VISAP).