Decoding Everything Everywhere All at Once by Angelica Hom

“Decoding Everything Everywhere All at Once” was my thesis project at Parsons. The inspiration came from personal experience of really loving the movie but still feeling confused by the end. When I Google’d, “Meaning of bagels or googly eyes?”, I noticed two issues: (1) There were lots of written articles that analyzed the movie but didn’t include visualizations (2) and most data visualizations about movies usually focus on industry metrics like box office profits instead of a narrative analysis. My project is a visual guide for viewers who want to better understand the movie.

Before I even started watching the film, I needed to understand what is involved in film analysis. I began researching film theory concepts like the difference between the three-act and 15-beat screenwriting structures, or what makes semiotic analysis different from mise-en-scene analysis from narrative analysis. I also researched art movements in film history (e.g. surrealism, poetic realism, independent cinema) and film techniques (e.g. continuity editing, studio realism) that were influential to Everything Everywhere All at Once thanks to David Parkinson’s 100 Ideas that Changed Film.

In addition to film theory, I researched other data visualization pieces that focused on one movie or show to see how they approached their project. My main sources of inspiration came from Fluid Encodings’ A Visual Guide to The Big Lebowski, a Parsons paper by Rasagy Sharma and Venkatesh Rajamanickam called Using Interactive Data Visualization to Explore Non-Linear Movie Narratives, and The Pudding’s The Structure of Stand-Up Comedy. What I liked about these three projects was that they focused on complex narrative structures, which made Everything Everywhere All at Once the next perfect case study. I learned that they had to manually collect their data, which makes sense given that most movie datasets that are publicly available focus on industry metrics like profits or ratings.

I had a solid strategy to start my project: take a narrative analysis approach to Everything Everywhere All at Once by transcribing and time-stamping every line of the movie, then add metadata to it to see if there are patterns in the characters, themes, structure, and universes. I used Google Sheets to create my primary dataset and also because it wasn’t going to be a large dataset that needed complex programming like SQL. Metadata included which character was speaking, to whom they were speaking to, which universe they were in, and even a sentiment analysis score using ChatGPT and Google Colab following this tutorial.

A lot of thought and background research through numerous interviews and articles went into doing a traditional academic film analysis of the Everything Everywhere All at Once. In fact, my thesis paper is 90 pages long! If you’d like to read more about the final analysis and the design process of the visualizations, please visit