# Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences

arXiv: https://arxiv.org/abs/1804.07000 · HTML: https://mlsanity.com/paper/1804.07000/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2018.

## Built on by (most cited first, 30 of 36)

- [Symptom Identification for Interpretable Detection of Multiple Mental Disorders](https://mlsanity.com/paper/2205.11308/) (2022)
- [It's Just a Matter of Time: Detecting Depression with Time-Enriched Multimodal Transformers](https://mlsanity.com/paper/2301.05453/) (2023)
- [Early Risk Detection of Pathological Gambling, Self-Harm and Depression Using BERT](https://mlsanity.com/paper/2106.16175/) (2021)
- [Read, Diagnose and Chat: Towards Explainable and Interactive LLMs-Augmented Depression Detection in Social Media](https://mlsanity.com/paper/2305.05138/) (2023)
- [Synthetic Data Generation with LLM for Improved Depression Prediction](https://mlsanity.com/paper/2411.17672/) (2024)
- [Calibration of Transformer-based Models for Identifying Stress and Depression in Social Media](https://mlsanity.com/paper/2305.16797/) (2023)
- [Psychiatric Scale Guided Risky Post Screening for Early Detection of Depression](https://mlsanity.com/paper/2205.09497/) (2022)
- [DepressionNet: A Novel Summarization Boosted Deep Framework for Depression Detection on Social Media](https://mlsanity.com/paper/2105.10878/) (2021)
- [DepressionEmo: A novel dataset for multilabel classification of depression emotions](https://mlsanity.com/paper/2401.04655/) (2024)
- [Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processes](https://mlsanity.com/paper/2212.02055/) (2022)
- [Semantic Similarity Models for Depression Severity Estimation](https://mlsanity.com/paper/2211.07624/) (2022)
- [Exploring Social Media for Early Detection of Depression in COVID-19 Patients](https://mlsanity.com/paper/2302.12044/) (2023)
- [Utilizing ChatGPT Generated Data to Retrieve Depression Symptoms from Social Media](https://mlsanity.com/paper/2307.02313/) (2023)
- [Assessing ML Classification Algorithms and NLP Techniques for Depression Detection: An Experimental Case Study](https://mlsanity.com/paper/2404.04284/) (2024)
- [Towards transparency in NLP shared tasks](https://mlsanity.com/paper/2105.05020/) (2021)
- [DepressionX: Knowledge Infused Residual Attention for Explainable Depression Severity Assessment](https://mlsanity.com/paper/2501.14985/) (2025)
- [Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media](https://mlsanity.com/paper/2510.14889/) (2025)
- [Mental Disorder Classification via Temporal Representation of Text](https://mlsanity.com/paper/2406.15470/) (2024)
- [Deep Knowledge-Infusion For Explainable Depression Detection](https://mlsanity.com/paper/2409.02122/) (2024)
- [Sentiment Informed Sentence BERT-Ensemble Algorithm for Depression Detection](https://mlsanity.com/paper/2409.13713/) (2024)
- [Depression detection in social media posts using transformer-based models and auxiliary features](https://mlsanity.com/paper/2409.20048/) (2024)
- [Automatic Depression Assessment using Machine Learning: A Comprehensive Survey](https://mlsanity.com/paper/2506.18915/) (2025)
- [Assessing Algorithmic Bias in Language-Based Depression Detection: A Comparison of DNN and LLM Approaches](https://mlsanity.com/paper/2509.25795/) (2025)
- [Personality-guided Public-Private Domain Disentangled Hypergraph-Former Network for Multimodal Depression Detection](https://mlsanity.com/paper/2511.12460/) (2025)
- [Mental Multi-class Classification on Social Media: Benchmarking Transformer Architectures against LSTM Models](https://mlsanity.com/paper/2509.16542/) (2025)
- [CNSocialDepress: A Chinese Social Media Dataset for Depression Risk Detection and Structured Analysis](https://mlsanity.com/paper/2510.11233/) (2025)
- [A Safety-Aware Role-Orchestrated Multi-Agent LLM Framework for Behavioral Health Communication Simulation](https://mlsanity.com/paper/2604.00249/) (2026)
- [Social Behavior and Mental Health: A Snapshot Survey under COVID-19 Pandemic](https://mlsanity.com/paper/2105.08165/) (2021)
- [Machine Learning Algorithms for Depression Detection and Their Comparison](https://mlsanity.com/paper/2301.03222/) (2023)
- [Using Mobile Data and Deep Models to Assess Auditory Verbal Hallucinations](https://mlsanity.com/paper/2304.11049/) (2023)

## Abstract (arXiv)

Depression is ranked as the largest contributor to global disability and is also a major reason for suicide. Still, many individuals suffering from forms of depression are not treated for various reasons. Previous studies have shown that depression also has an effect on language usage and that many depressed individuals use social media platforms or the internet in general to get information or discuss their problems. This paper addresses the early detection of depression using machine learning models based on messages on a social platform. In particular, a convolutional neural network based on different word embeddings is evaluated and compared to a classification based on user-level linguistic metadata. An ensemble of both approaches is shown to achieve state-of-the-art results in a current early detection task. Furthermore, the currently popular ERDE score as metric for early detection systems is examined in detail and its drawbacks in the context of shared tasks are illustrated. A slightly modified metric is proposed and compared to the original score. Finally, a new word embedding was trained on a large corpus of the same domain as the described task and is evaluated as well.