Abstract :

With The Increasing Use Of Social Networks, Users Share Extensive Personal Content That Reflects Their Emotional And Psychological States. This Study Focuses On Detecting Stress Levels By Analyzing Social Interactions On Platforms Such As Twitter, Facebook, And Instagram. The Proposed System Leverages Machine Learning And Natural Language Processing (NLP) Techniques To Examine Textual Posts, Comments, And Engagement Patterns, Identifying Stress-related Cues Through Sentiment Analysis, Linguistic Markers, And Behavioral Features. Graph-based Analysis Of User Interactions And Network Centrality Measures Are Incorporated To Assess The Influence Of Social Connections On Stress Levels. By Detecting Stress In Real Time, This Approach Can Support Mental Health Monitoring, Early Intervention, And Personalized Recommendations. The Research Demonstrates That Analyzing Social Interactions On Digital Platforms Provides A Scalable And Effective Method For Understanding User Stress Patterns. Keywords: Stress Detection, Social Networks, Social Interaction Analysis, Machine Learning, NLP, Sentiment Analysis, Behavioral Features, Mental Health Monitoring, Real-Time Detection, Graph Analysis.

Published:

28-10-2025

Issue:

Vol. 25 No. 10 (2025)

Page Nos:

152-157

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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