# 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