Active and Passive Patterns of Platform-Based Social Media Engagement among Anxious Young Adults

Abstract Number:

1949 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Ying Chen (1), Bin Cheng (2), Cristiane Duarte (3), Jazmin Portillo (4)

Institutions:

(1) N/A, N/A, (2) Columbia University, N/A, (3) Columbia University, New York, NY, (4) New York State Psychiatric Institute, New York, NY

Co-Author(s):

Bin Cheng  
Columbia University
Cristiane Duarte  
Columbia University
Jazmin Portillo  
New York State Psychiatric Institute

First Author:

Ying Chen  
N/A

Presenting Author:

Ying Chen  
N/A

Abstract Text:

Increased time on social media platforms (SMP) has contributed to mental health crisis, particularly among youth. This study aims to identify different types of SMP use engagement patterns (e.g., passive versus active) and investigate their relationship with anxiety symptoms among emerging young adults. Participants provided their SMP use data from Facebook, Instagram, Snapchat, Twitter, and YouTube. Generalized Linear Models (GLM) with Tweedie distribution were constructed to model outgoing engagement variabilities between passive and active use. Growth Mixture Models (GMMs) were also applied to identify latent SMP use patterns that maybe related to participants' anxiety symptoms. Daily outgoing variation between active and passive engagement was associated with anxiety score at follow-up, meaning more variation was associated with less anxiety symptoms. 3-Class latent patterns were identified by GMMs using overall data or split by daytime or nighttime use, and significant association with anxiety symptoms at follow-up were identified. SMP use and its impact on youth are important. Future applications will apply these methods to college students and other mental health domains.

Keywords:

Social Media|Anxiety|Tweedie Distribution|Growth Mixture Models|Latent Groups|Patterns

Sponsors:

Mental Health Statistics Section

Tracks:

Latent variable and measurement modeling

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