FoodCensor: Promoting Mindful Digital Food Content Consumption for People with Eating Disorders

_{Figure. FoodCensor deployed on a Chrome browser. Snapshot of the YouTube Home page including food content without FoodCensor (1) and with FoodCensor (2). FoodCensor conceals Filter buttons, video thumbnails, and Shorts video thumbnails showing food content and disables clicking them.}

This repository is the Chrome Extension version of FoodCensor (FoodCensor: Promoting Mindful Digital Food Content Consumption for People with Eating Disorders, ACM CHI'24). This code can be adapted to censor YouTube videos on specific topics by modifying the keywords used to filter content based on textual descriptions (e.g., video titles).

For more information about this project, please visit https://nmsl.kaist.ac.kr/projects/foodcensor/

How to Build and Run

System Overview

FoodCensor is a stand-alone application that does not require a backend server. The app reads YouTube webpage on Google Chrome browser locally and overlays intervention screens.

Prerequisites

• Google Chrome
• Developer mode enabled in Chrome (refer to Step 2.2 below)

Steps to Build and Run

1. Download the Repository
   • Download or clone this repository to your local machine.
     • *You don't need assets to execute FoodCensor Chrome extension.
2. Load the Extension in Chrome
   1. Open Chrome and navigate to chrome://extensions/.
   2. Enable Developer mode (toggle in the top right corner).

      

   3. Click Load unpacked.

      

   4. Select the project folder.
3. Use the Extension
   • FoodCensor will filter YouTube videos based on both English and Korean food-related keywords.

Uninstallation

• Go to chrome://extensions/, find the FoodCensor extension, and click Remove.

Related Research Papers

FoodCensor: Promoting Mindful Digital Food Content Consumption for People with Eating Disorders
Ryuhaerang Choi, Subin Park, Sujin Han, and Sung-Ju Lee
ACM CHI 2024 (PDF)

How People with Eating Disorders Get Trapped in the Perpetual Cycle of Digital Food Content
Ryuhaerang Choi, Subin Park, Sujin Han, and Sung-Ju Lee
(arXiv)

Research Team Members

Ryuhaerang Choi 🔗
Ph.D. Student
KAIST
Contact for code and implementation

Subin park 🔗
M.S. Student
KAIST

Sujin Han 🔗
M.S.-Ph.D. Integrated Student
KAIST

Sung-Ju Lee 🔗
Professor
KAIST

Acknowledgment

This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2022-0-00064, Development of Human Digital Twin Technologies for Prediction and Management of Emotion Workers’ Mental Health Risks).