Mental Health and TikTok

Project Information

Project Status: Complete
Project Region: CAREERS
Submitted By: Gaurav Khanna
Project Email: akothari@uri.edu
Project Institution: University of Rhode Island -- Harrington School
Anchor Institution: CR-University of Rhode Island
Project Address: Rhode Island

Mentors: Ammina Kothari
Students: Nathaniel Nichols

Project Description

Our project explores the mental health discourse on TikTok in three languages - English, Portuguese, and Arabic to examine linguistic and visual differences in how mental health-related topics are presented. The platform's popularity with children and young adults offers a unique opportunity to identify the popular accounts receiving the most engagement and the types of messages and topics highlighted across three languages. While attention on social media is difficult to measure precisely, given multiple measurements available on Twitter/X or Facebook, and Instagram to indicate engagement, TikTok's algorithm-driven content presentation based on an individual's indicated preferences and previously liked content gives a more precise understanding of what content is going viral or getting attention.

The student will learn to use various computational tools to collect TikTok videos and Video AI by Google to recognize objects, places, and actions in the videos, extract transcripts for topic modeling, and further content analysis. The student facilitator will learn how to use the URI UNITY cluster effectively for analysis and work with the research team to complete the project in a timely manner.

Project Information

Project Status: Complete
Project Region: CAREERS
Submitted By: Gaurav Khanna
Project Email: akothari@uri.edu
Project Institution: University of Rhode Island -- Harrington School
Anchor Institution: CR-University of Rhode Island
Project Address: Rhode Island

Mentors: Ammina Kothari
Students: Nathaniel Nichols

Project Description

Our project explores the mental health discourse on TikTok in three languages - English, Portuguese, and Arabic to examine linguistic and visual differences in how mental health-related topics are presented. The platform's popularity with children and young adults offers a unique opportunity to identify the popular accounts receiving the most engagement and the types of messages and topics highlighted across three languages. While attention on social media is difficult to measure precisely, given multiple measurements available on Twitter/X or Facebook, and Instagram to indicate engagement, TikTok's algorithm-driven content presentation based on an individual's indicated preferences and previously liked content gives a more precise understanding of what content is going viral or getting attention.

The student will learn to use various computational tools to collect TikTok videos and Video AI by Google to recognize objects, places, and actions in the videos, extract transcripts for topic modeling, and further content analysis. The student facilitator will learn how to use the URI UNITY cluster effectively for analysis and work with the research team to complete the project in a timely manner.