# Towards Preemptive Detection of Depression and Anxiety in Twitter

arXiv: https://arxiv.org/abs/2011.05249 · HTML: https://mlsanity.com/paper/2011.05249/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2020.

## Built on by (most cited first, 13 of 13)

- [Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts](https://mlsanity.com/paper/2307.02768/) (2023)
- [Depression Detection on Social Media with Large Language Models](https://mlsanity.com/paper/2403.10750/) (2024)
- [Still Not Quite There! Evaluating Large Language Models for Comorbid Mental Health Diagnosis](https://mlsanity.com/paper/2410.03908/) (2024)
- [DepressionEmo: A novel dataset for multilabel classification of depression emotions](https://mlsanity.com/paper/2401.04655/) (2024)
- [A Comprehensive Evaluation of Large Language Models on Mental Illnesses](https://mlsanity.com/paper/2409.15687/) (2024)
- [Utilizing ChatGPT Generated Data to Retrieve Depression Symptoms from Social Media](https://mlsanity.com/paper/2307.02313/) (2023)
- [A Domain-Agnostic Neurosymbolic Approach for Big Social Data Analysis: Evaluating Mental Health Sentiment on Social Media during COVID-19](https://mlsanity.com/paper/2411.07163/) (2024)
- [Datasets for Depression Modeling in Social Media: An Overview](https://mlsanity.com/paper/2503.21513/) (2025)
- [Exploring Large Language Models for Detecting Mental Disorders](https://mlsanity.com/paper/2410.07129/) (2024)
- [Mental-R1: Aligning LLM Reasoning for Mental Health Assessment](https://mlsanity.com/paper/2606.13176/) (2026)
- [Deteksi Depresi dan Kecemasan Pengguna Twitter Menggunakan Bidirectional LSTM](https://mlsanity.com/paper/2301.04521/) (2023)
- [MAQuA: Adaptive Question-Asking for Multidimensional Mental Health Screening using Item Response Theory](https://mlsanity.com/paper/2508.07279/) (2025)
- [Analysing Self-Harm Representations in Language Models: a Cross-Architecture Study](https://mlsanity.com/paper/2607.21988/) (2026)

## Abstract (arXiv)

Depression and anxiety are psychiatric disorders that are observed in many areas of everyday life. For example, these disorders manifest themselves somewhat frequently in texts written by nondiagnosed users in social media. However, detecting users with these conditions is not a straightforward task as they may not explicitly talk about their mental state, and if they do, contextual cues such as immediacy must be taken into account. When available, linguistic flags pointing to probable anxiety or depression could be used by medical experts to write better guidelines and treatments. In this paper, we develop a dataset designed to foster research in depression and anxiety detection in Twitter, framing the detection task as a binary tweet classification problem. We then apply state-of-the-art classification models to this dataset, providing a competitive set of baselines alongside qualitative error analysis. Our results show that language models perform reasonably well, and better than more traditional baselines. Nonetheless, there is clear room for improvement, particularly with unbalanced training sets and in cases where seemingly obvious linguistic cues (keywords) are used counter-intuitively.