# Decoding Linguistic Nuances in Mental Health Text Classification Using Expressive Narrative Stories

arXiv: https://arxiv.org/abs/2412.16302 · HTML: https://mlsanity.com/paper/2412.16302/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 20 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://mlsanity.com/paper/2110.15621/) (2021)
- [Depression and Self-Harm Risk Assessment in Online Forums](https://mlsanity.com/paper/1709.01848/) (2017)
- [Data set creation and empirical analysis for detecting signs of depression from social media postings](https://mlsanity.com/paper/2202.03047/) (2022)
- [Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media](https://mlsanity.com/paper/1710.05429/) (2017)
- [A Multitask Deep Learning Approach for User Depression Detection on Sina Weibo](https://mlsanity.com/paper/2008.11708/) (2020)

## Built on by (most cited first, 1 of 1)

- [DepressLLM: Interpretable domain-adapted language model for depression detection from real-world narratives](https://mlsanity.com/paper/2508.08591/) (2025)

## Abstract (arXiv)

Recent advancements in NLP have spurred significant interest in analyzing social media text data for identifying linguistic features indicative of mental health issues. However, the domain of Expressive Narrative Stories (ENS)-deeply personal and emotionally charged narratives that offer rich psychological insights-remains underexplored. This study bridges this gap by utilizing a dataset sourced from Reddit, focusing on ENS from individuals with and without self-declared depression. Our research evaluates the utility of advanced language models, BERT and MentalBERT, against traditional models. We find that traditional models are sensitive to the absence of explicit topic-related words, which could risk their potential to extend applications to ENS that lack clear mental health terminology. Despite MentalBERT is design to better handle psychiatric contexts, it demonstrated a dependency on specific topic words for classification accuracy, raising concerns about its application when explicit mental health terms are sparse (P-value<0.05). In contrast, BERT exhibited minimal sensitivity to the absence of topic words in ENS, suggesting its superior capability to understand deeper linguistic features, making it more effective for real-world applications. Both BERT and MentalBERT excel at recognizing linguistic nuances and maintaining classification accuracy even when narrative order is disrupted. This resilience is statistically significant, with sentence shuffling showing substantial impacts on model performance (P-value<0.05), especially evident in ENS comparisons between individuals with and without mental health declarations. These findings underscore the importance of exploring ENS for deeper insights into mental health-related narratives, advocating for a nuanced approach to mental health text analysis that moves beyond mere keyword detection.