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Detecting Risky Health Behaviors in TikTok Videos

The "Detecting Risky Health Behaviors in TikTok Videos" dataset is a collection of TikTok video metadata and annotations that aims to facilitate the development of ML models that can detect and classify risky health behaviors depicted in short-form TikTok videos.

Dataset Description

All videos included in this dataset can be found under this Drive Folder: https://drive.google.com/drive/folders/1LUStRDMuE0FDGtHEYJq684_jJOaw7gsQ?usp=drive_link

Our dataset include the following columns:

  • ID: A unique identifier for each video entry.
  • createTimeISO: The timestamp when the video was created, formatted in ISO 8601 standard.
  • commentCount: The total number of comments received on the video.
  • diggCount: The number of "diggs" or likes the video has received.
  • playCount: The total number of times the video has been played.
  • shareCount: The number of times the video has been shared by viewers.
  • text: The caption associated with the video.
  • webVideoUrl: URL to view the video on TikTok.
  • Split: Indicates the dataset division for each entry.
  • Risky Behavior: A binary label indicating whether the video depicts risky health behavior (TRUE) or not (FALSE) (Stored in a different csv file).
  • Video Name: The name under which the video was saved on Drive (Under the folders Train or Validation or Test).

Dataset Collection

The dataset was collected on October 1st, 2024 from TikTok using the Apify TikTok Hashtag Scraper tool. Here are the detailed steps for data collection:

  • Access the Apify TikTok Hashtag Scraper.
  • Input the desired hashtags. In this case, the following hashtags were used: #cigarettes, #alcohol, #diet, #workout, #food, #smoking.
  • Run the scraper for each hashtag, limiting the number of collected videos to 100 videos per hashtag to ensure a manageable dataset size.
  • After scraping, export the data as a CSV file.

I retained the following columns from the scraped data for further analysis:

Annotations

Annotation process

The annotators of this dataset were asked to watch no more than 30 seconds of each TikTok video, and determine whether the video depicts or mentions a risky health behavior.

  • Unrisky Behavior: The video does not show or mention any risky health behavior or the video discourage an unhealthy behavior/ encourage a healthy behavior. This could include either positive or neutral behaviors.
  • Risky Behavior: The video shows or mentions smoking (e.g., smoking cigarettes, vaping), or/and the video shows or mentions alcohol consumption (e.g., drinking beer, wine, or any alcoholic beverages), or/and the video shows or mentions unhealthy eating habits (e.g., Overconsumption of Junk/Processed Foods, Excessive Intake of Sugary Beverages, Crash Diets or Extreme Caloric Restriction, Overeating or Encouraging Binge Eating, Neglecting Essential Nutrients, Promoting Harmful Dietary Supplements), or/and the video shows or mentions any other type of risky health behavior (e.g., reckless behavior, drug use, or similar health risks).

Codalab Competition Leaderboard

https://codalab.lisn.upsaclay.fr/competitions/20634#learn_the_details