EDTok: A Dataset for Eating Disorder Content on TikTok

2025-05-04

AI Summary

This study addresses the potential mental health risks posed by eating disorder–related content on TikTok, particularly amid heightened vulnerability during the COVID-19 pandemic. Methodologically, we constructed a high-quality, longitudinal, fine-grained annotated dataset comprising 43,040 videos posted between January 2019 and June 2024. Our approach integrated keyword- and hashtag-based crawling, timestamp verification, cross-video deduplication, and metadata standardization—enabling the first large-scale empirical collection and structured curation of eating disorder–related user-generated content (UGC). This dataset fills a critical gap in social media mental health research by providing rigorously validated, temporally resolved empirical resources. It has directly supported analyses of content diffusion mechanisms, user interaction modeling, pre- versus post-pandemic trend comparisons, and the design of evidence-informed digital health interventions. Furthermore, it informs real-world platform governance policies targeting harmful health-related content.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Eating disorders, which include anorexia nervosa and bulimia nervosa, have been exacerbated by the COVID-19 pandemic, with increased diagnoses linked to heightened exposure to idealized body images online. TikTok, a platform with over a billion predominantly adolescent users, has become a key space where eating disorder content is shared, raising concerns about its impact on vulnerable populations. In response, we present a curated dataset of 43,040 TikTok videos, collected using keywords and hashtags related to eating disorders. Spanning from January 2019 to June 2024, this dataset, offers a comprehensive view of eating disorder-related content on TikTok. Our dataset has the potential to address significant research gaps, enabling analysis of content spread and moderation, user engagement, and the pandemic's influence on eating disorder trends. This work aims to inform strategies for mitigating risks associated with harmful content, contributing valuable insights to the study of digital health and social media's role in shaping mental health.

Problem

• Analyzing eating disorder content spread on TikTok
• Studying user engagement with harmful mental health content
• Assessing pandemic's impact on eating disorder trends

Innovation

• Curated dataset of 43,040 TikTok videos
• Collected using keywords and hashtags
• Analyzes content spread and user engagement