A Multivariate Analysis of Social Media’s Predictive Impact on

Adolescent Depression and Anxiety

Xinrui Zhang

Sanhe No.2 Middle School, Langfang, 065200, China

Keywords: Adolescent Mental Health, Social Media Engagement, Depression, Regression Analysis, Algorithmic

Stressors.

Abstract: Adolescent mental health, particularly depression and anxiety, has emerged as a critical public health concern

in the digital era. This study examines the predictive relationships between social media engagement-

quantified through daily usage time, platform diversity, and interaction frequency-and mental health outcomes

among adolescents, while accounting for demographic heterogeneity. Data from the Adolescent Mental

Health and Social Media Use Survey (AMHSM-2023; N = 1,050) were analysed using multiple linear

regression and stratified sensitivity analyses. Prolonged social media use demonstrated significant

associations with elevated depression (β = 0.31, p < 0.001) and anxiety (β = 0.25, p < 0.001). Gender and

environmental factors moderated these relationships, with female and urban adolescents exhibiting heightened

vulnerability. Platform diversity displayed a nonlinear association with depression, suggesting an optimal

engagement range. These findings advocate for balanced digital engagement policies and underscore the

importance of interpretable models for targeted interventions. By integrating psychometric and computational

methodologies, this study advances a holistic framework for addressing algorithmic stressors in adolescent

mental health.

1 INTRODUCTION

Over the past two decades, adolescent depression and

anxiety have emerged as critical public health

challenges, with profound implications for immediate

well-being and long-term mental health trajectories

into adulthood (Thapar et al., 2012). The

multifactorial etiology of these conditions has spurred

extensive research, encompassing genetic

predispositions, cognitive vulnerabilities, familial

dynamics, neurobiological mechanisms, and, more

recently, the pervasive influence of digital

environments (Hankin and Abramson, 2001).

Traditional developmental studies emphasized

cognitive and gender-specific risk factors, particularly

the interplay between negative cognitive schemas and

pubertal transitions during adolescence (Beesdo et al.,

2009). However, contemporary scholarship

increasingly underscores the role of sociotechnical

systems, such as social media platforms, in reshaping

adolescent mental health landscapes (Keles et al.,

2020; Muthén, 2002). This paradigm shift necessitates

https://orcid.org/0009-0004-9596-946X

a synthesis of psychosocial frameworks with data-

driven methodologies to disentangle complex risk

interactions and advance predictive models for early

intervention.

Historically, family environments have been

identified as pivotal moderators of mental health

outcomes. Longitudinal studies by Repetti et al.

demonstrated that familial stressors, including conflict

and emotional neglect, exacerbate depressive and

anxious symptoms through dysregulated stress

response systems (Repetti et al., 2002). Similarly,

developmental psychopathology research highlights

bidirectional relationships between cognitive

vulnerabilities (e.g., rumination, attentional biases)

and environmental stressors, with gender-specific

pathways further complicating risk profiles (Beesdo et

al., 2009). These insights, however, predominantly

derive from small-scale, hypothesis-driven studies

employing structural equation modeling (SEM) or

longitudinal designs (Li and Lu, 2018). While robust

for testing predefined pathways, such approaches

often struggle to capture dynamic, non-linear

Zhang, X.

A Multivariate Analysis of Social Media’s Predictive Impact on Adolescent Depression and Anxiety.

DOI: 10.5220/0013825100004708

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd Inter national Conference on Innovations in Applied Mathematics, Physics, and Astronomy (IAMPA 2025), pages 319-323

ISBN: 978-989-758-774-0

319

interactions characteristic of real-world psychosocial

ecosystems.

The advent of digital media has introduced novel

risk dimensions that transcend traditional frameworks.

Empirical analyses leveraging large-scale datasets

reveal significant associations between heavy social

media use and elevated psychological distress,

particularly among adolescents (Keles et al., 2020).

For instance, systematic reviews identify dose-

response relationships between platform engagement

metrics (e.g., daily usage time, cyberbullying

exposure) and deteriorations in self-esteem and

emotional regulation (McLaughlin and King, 2015).

Concurrently, advances in computational social

science enable researchers to employ machine

learning (ML) techniques-such as random forests and

neural networks-to predict mental health outcomes

from digital trace data (Muthén, 2002). Meta-analyses

of ML applications report improved predictive

accuracy for depression when integrating multimodal

data (e.g., screen time patterns, linguistic cues)

(McLaughlin and King, 2015). Yet, these models

frequently prioritize predictive power over

interpretability, limiting their utility for identifying

actionable intervention targets.

Critically, the interconnectedness of anxiety and

depression symptomatology complicates etiological

disentanglement. Developmental studies emphasize

the high comorbidity of these conditions, advocating

for integrated models that account for shared and

unique risk factors across diagnostic boundaries (Li

and Lu, 2018). While multivariate approaches, such as

latent variable modeling, have been proposed to

address this complexity, their implementation in

digital mental health research remains nascent

(Costello and Angold, 2006). Moreover, existing

studies often overlook demographic heterogeneity-

such as age- and gender-specific vulnerabilities-

despite evidence that adolescents exhibit divergent

mental health trajectories based on developmental

stage and identity (Bor et al., 2014; Hankin and

Abramson, 2001). For example, longitudinal cohorts

identify escalating anxiety rates among female

adolescents, a trend exacerbated by social media-

driven social comparison processes (McLaughlin and

King, 2015).

2 METHODOLOGY

2.1 Data Source and Description

The dataset was derived from the AMHSM-2023

survey, a cross-sectional study of 1200 U.S.

adolescents aged 13-18. After excluding incomplete

responses, 1050 participants were retained for

analysis. Stratified random sampling ensured

representativeness across age, gender, and

socioeconomic status. Variables included social

media engagement metrics, mental health scores

(PHQ-9 for depression, GAD-7 for anxiety), and

demographic data.

2.2 Indicator Selection and

Explanation

Key variables were selected based on theoretical

relevance to social media use and mental health

outcomes. A three-line table (Table 1) summarizes

the indicators, their definitions, and quantification

methods.

Table 1: Operational definitions and measurement scales

of study variables

Variable Definition

Measurement

Scale

Daily

Usage

Time

Average hours spent on

social media daily

Continuous(0-

12 hours)

Platform

Diversit

Number of platforms

used re

ularl

Count (1–8

latforms

)

Interacti

Frequenc

Frequency of

likes/comments/sharing

Ordinal

(1=Never,

5=Daily)

Depressi

on Score

PHQ-9 (Patient Health

Questionnaire-9

)

Summative (0–

oints

)

Anxiety

Score

GAD-7 (Generalized

Anxiet

Disorde

-7

)

Summative (0–

oints

)

Age Participant’s age

Continuous

(

–

ears

)

Gender

Self-reported gender

identity

Categorical

(Male/Female/N

on-

inary)

2.3 Analytical Framework

To examine the associations between social media

engagement and adolescent mental health outcomes,

two multiple linear regression models were

constructed-one for depression and one for anxiety:

Depression = 𝛽



+𝛽



𝑋



+𝛽



𝑋



+𝛽



𝑋



+𝛽



𝑋



𝛽



𝐷Female + 𝛽



𝐷Non_binary + 𝜖 (1)

Anxiety = 𝛾



+𝛾



𝑋



+𝛾



𝑋



+𝛾



𝑋



+𝛾



𝑋



𝛾



𝐷Female + 𝛾



𝐷Non_binary + ϵ (2)

IAMPA 2025 - The International Conference on Innovations in Applied Mathematics, Physics, and Astronomy

320

Where 𝑋



represents standardized daily social media

usage time, 𝑋



denotes platform diversity (i.e., the

number of platforms used regularly), 𝑋



captures

interaction frequency (including liking, commenting,

and sharing), and 𝑋



corresponds to the participant's

age. Gender was operationalized using two dummy

variables: DFemale and DNon_binary, with male as

the reference category.

Both models were estimated using ordinary least

squares regression. Prior to analysis, multicollinearity

was assessed using the Variance Inflation Factor

(VIF), and all predictors were confirmed to have VIF

values below 5, indicating acceptable levels of

collinearity. To evaluate model performance and

generalizability, a 70-30 train-test split was applied,

in which 70% of the sample was used for training and

30% for testing. This modeling approach balances

interpretability with statistical rigor, enabling the

identification of key behavioral and demographic

predictors of mental health symptoms among

adolescents.

3 RESULTS AND DISCUSSION

3.1 Descriptive Statistics and

Preliminary Analysis

The final analytic sample comprised 1,050

adolescents aged 13 to 18 years, with a mean age of

15.4 years (SD = 1.8). In terms of gender distribution,

52% identified as female, 45% as male, and 3% as

non-binary. On average, participants reported

spending 3.6 hours per day on social media (SD = 2.1)

and regularly using approximately 4.2 different

platforms (SD = 1.5), reflecting diverse and frequent

engagement with digital media.

Mental health indicators revealed a concerning

level of psychological distress among participants.

The average depression score, as measured by the

PHQ-9, was 11.5 (SD = 5.3), while the average

anxiety score, assessed using the GAD-7, was 9.2 (SD

= 4.8). These means fall within the mild-to-moderate

clinical range, suggesting that a substantial proportion

of adolescents in the sample may be experiencing

significant mental health challenges.

Visual trends support these findings. There is a

clear positive association between daily social media

usage time and depression scores (Figure 1).

Participants who spent more time on social media

reported higher levels of depressive symptoms,

indicating a potential dose-response relationship

between usage duration and emotional well-being.

This trend supports prior literature suggesting that

prolonged exposure to curated content and peer

feedback online may exacerbate emotional

vulnerabilities.

Further differences emerged across gender

groups. Figure 2 illustrates mean daily social media

usage time by gender. Notably, usage time was lowest

among male adolescents, slightly higher among

female participants, and highest among non-binary

individuals. This gradient suggests varying patterns

of digital engagement across gender identities, which

may help explain subsequent differences in mental

health outcomes analyzed in later sections. Such

disparities underscore the importance of adopting an

intersectional approach when examining the impact

of social media on adolescent well-being.

Figure 1: Trends in depression scores across different

levels of daily social media usage (x-axis: daily social

media usage time, unit: h; y-axis: depression score)

(Picture credit: Original).

Figure 2: Mean daily social media usage across gender

groups (Picture credit: Original).

3.4

3.7

3.9

3.1

3.2

3.3

3.4

3.5

3.6

3.7

3.8

3.9

Male Female Non- Binary

Daily usage time

A Multivariate Analysis of Social Media’s Predictive Impact on Adolescent Depression and Anxiety

321

3.2 Regression Analysis

Both models explained significant variance (Table 2).

Daily usage time was the strongest predictor: each

additional hour increased depression by 1.2 points (β

= 0.31, p < 0.001) and anxiety by 0.9 points (β = 0.25,

p < 0.001). Platform diversity exhibited a nonlinear

effect, with moderate use (4–5 platforms) associated

with lower depression (β = -0.12, p = 0.03).

Interaction frequency predicted anxiety (β = 0.18, p =

0.02) but not depression.

Table 2: Standardized regression coefficients predicting

depression and anxiety.

Variable

Depression

(PHQ-9) β

Anxiety

(GAD-7) β

Daily Usage Time 0.31 0.25

Platform Diversity -0.12 -0.08

Interaction

Fre

uenc

0.09 0.18

Age 0.05 0.1

Gender (Female) 0.22 0.27

Adjusted R² 0.36 0.29

3.3 Stratified Sensitivity Analysis

Gender-stratified models revealed divergent

pathways: interaction frequency strongly predicted

anxiety in females (β = 0.31, p < 0.001) but not males.

Urban adolescents exhibited stronger associations

between usage time and depression (β = 0.35, p <

0.001) compared to rural peers (β = 0.18, p = 0.04).

3.4 Discussion

The findings corroborate hypotheses that intensive

social media use exacerbates adolescent mental

health risks, particularly through prolonged exposure

to curated content and feedback loops (McLaughlin,

2015). The nonlinear relationship between platform

diversity and depression underscores the need for

balanced digital engagement, aligning with Li and

Lu’s advocacy for "quality over quantity" in screen

time (Li and Lu, 2018). Gender disparities in

sensitivity to social interactions echo developmental

theories emphasizing female adolescents’ heightened

reactivity to peer evaluation (Hankin and Abramson,

2001). This could stem from that girls are socialized

to pay more attention to relationships and external

validation, making them more sensitive to the

judgments they get on social media.

Moreover, findings of this study reveal notable

differences between adolescents from urban and rural

environments, particularly in reported anxiety levels.

Urban adolescents exhibited significantly higher

scores, potentially reflecting the compounded effects

of higher digital connectivity, competitive academic

settings, and denser social networks that amplify

perceived social pressures. In contrast, rural

participants, though not immune to social media

exposure, may benefit from comparatively buffered

offline environments that offer alternative sources of

emotional regulation and support.

Notably, the study’s integration of traditional

regression with stratified analysis advances

methodological rigor. While machine learning

models offer superior predictive power, approaches in

this study prioritizes interpretability, identifying

actionable levers for intervention (e.g., limiting daily

usage). These results suggest that digital well-being

interventions should consider not only screen time but

also the contextual and psychosocial factors that

modulate its impact (Li and Lu, 2018; McLaughlin

and King, 2015). However, the cross-sectional design

precludes causal inference, and self-reported data

may introduce response bias. Future longitudinal

studies should track dynamic interactions between

digital behaviors and mental health trajectories,

ideally incorporating ecological momentary

assessment to capture real-time fluctuations in

affective states.

4 CONCLUSION

This study elucidates the multifaceted relationships

between social media engagement and adolescent

mental health, emphasizing the roles of usage

intensity, demographic heterogeneity, and

environmental context. The robust association

between daily screen time and symptom severity

underscores the urgency of developing digital well-

being guidelines, particularly for high-risk subgroups

such as female and urban adolescents.

Gendered patterns in digital vulnerability suggest

the need for differentiated strategies-such as peer-

support interventions for girls or platform-specific

literacy training-to mitigate the psychological toll of

online interactions. Likewise, the urban–rural divide

signals that structural and cultural factors shape

adolescents' resilience or susceptibility to digital

stressors, highlighting the importance of localized

interventions and community-based mental health

resources.

IAMPA 2025 - The International Conference on Innovations in Applied Mathematics, Physics, and Astronomy

322

While machine learning offers predictive

advantages, regression-based approach used in this

study demonstrates that interpretable models remain

critical for translating data into actionable policies.

Future research should prioritize longitudinal designs

to disentangle causality and explore protective factors

(e.g., parental mediation, offline social support) that

buffer adolescents from the adverse effects of

excessive or emotionally charged digital engagement.

By integrating developmental psychology with

computational analytics, this work advances a holistic

understanding of algorithmic influences on mental

health, paving the way for scalable, evidence-based

interventions that are both socially aware and

clinically grounded.

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