Early Detection of Depression and Anxiety in Young Adults Using Machine Learning on Social Media and Survey Data

Abstract

Anxiety and depression among young adults have become serious public health concerns, yet early detection remains challenging due to the limitations of traditional mental health assessment methods. Conventional approaches rely mainly on self-reported questionnaires and face-to-face clinical evaluations, which are often conducted only after symptoms become severe. At the same time, young adults increasingly express their emotions, stress, and psychological distress through social media platforms such as Facebook, providing valuable real-world behavioral signals. However, most existing detection systems depend on a single data source, either social media text or psychological survey data, resulting in limited accuracy and an incomplete understanding of mental health conditions. Previous studies have showed that machine learning techniques can successfully identify linguistic patterns related to anxiety and depression from social media content. Similarly, standardized psychological assessment tools such as the Patient Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder scale (GAD-7) are widely recognized for measuring symptom severity through numerical scoring. Despite these advances, most prior research analyzes these data sources independently, with limited attention to integrated approaches that combine unstructured textual data with structured clinical information. To address this gap, This research establishes a highly effective hybrid framework for the early detection of anxiety and depression in young adults by integrating evidence from two complementary data sources. This study we proposeds a hybrid machine learning framework that integrates 6,877, Facebook posts with 103 there are 28 females and 75 male inside it self-report questionnaires based on the PHQ-9 and GAD-7 scales. A dual-model strategy was employed to accommodate the distinct characteristics of linguistic and numerical data. Different machine learning algorithms were evaluated for each data modality to identify optimal models. Experimental results demonstrate that Random Forest Machine (RF) achieves the highest accuracy 0.937821 for classifying anxiety and depression from Facebook text, while (SVM) provides accuracy 0.9905 superior performance for survey-based predictions. The fusion of these best-performing models significantly enhances overall detection accuracy, confirming that combining social media behavior with clinically validated psychological assessments offers a more reliable and comprehensive approach to early mental health risk detection among young adults. Keywords: Anxiety, Depression, Machine Learning, Social Media Analysis, Facebook posts, Hybrid model, Psychological questionnaires ,PHQ-9, GAD-7, Young Adults, Mental health assessment.