Early Linguistic Pattern of Anxiety from Social Media Using Interpretable Linguistic Features: A Multi-Faceted Validation Study with Author-Disjoint Evaluation

TL;DR

This study develops an interpretable, linguistically grounded model for detecting anxiety from social media posts, validated across multiple datasets and demonstrating robustness and early detection capabilities.

Contribution

It introduces a transparent, linguistically interpretable approach for social media-based anxiety detection with rigorous validation and robustness testing.

Findings

High accuracy maintained after sentiment removal

Early detection significantly outperforms random chance

Model generalizes well across domains and aligns with clinical data

Abstract

Anxiety affects hundreds of millions of individuals globally, yet large-scale screening remains limited. Social media language provides an opportunity for scalable detection, but current models often lack interpretability, keyword-robustness validation, and rigorous user-level data integrity. This work presents a transparent approach to social media-based anxiety detection through linguistically interpretable feature-grounded modeling and cross-domain validation. Using a substantial dataset of Reddit posts, we trained a logistic regression classifier on carefully curated subreddits for training, validation, and test splits. Comprehensive evaluation included feature ablation, keyword masking experiments, and varying-density difference analyses comparing anxious and control groups, along with external validation using clinically interviewed participants with diagnosed anxiety disorders. The model achieved strong performance while maintaining high accuracy even after sentiment removal or keyword masking. Early detection using minimal post history significantly outperformed random classification, and cross-domain analysis demonstrated strong consistency with clinical interview data. Results indicate that transparent linguistic features can support reliable, generalizable, and keyword-robust anxiety detection. The proposed framework provides a reproducible baseline for interpretable mental health screening across diverse online contexts.