# Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

arXiv: https://arxiv.org/abs/1710.05429 · HTML: https://mlsanity.com/paper/1710.05429/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2017.

## Built on by (most cited first, 24 of 24)

- [Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Posts](https://mlsanity.com/paper/2205.13884/) (2022)
- [Counseling Summarization using Mental Health Knowledge Guided Utterance Filtering](https://mlsanity.com/paper/2206.03886/) (2022)
- [Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS](https://mlsanity.com/paper/2104.04140/) (2021)
- [Emotion fusion for mental illness detection from social media: A survey](https://mlsanity.com/paper/2304.09493/) (2023)
- [DEPTWEET: A Typology for Social Media Texts to Detect Depression Severities](https://mlsanity.com/paper/2210.05372/) (2022)
- [DepressionNet: A Novel Summarization Boosted Deep Framework for Depression Detection on Social Media](https://mlsanity.com/paper/2105.10878/) (2021)
- [ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance](https://mlsanity.com/paper/2305.08010/) (2023)
- [A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework](https://mlsanity.com/paper/2203.11856/) (2022)
- [A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depression](https://mlsanity.com/paper/2311.13852/) (2023)
- [Modern Views of Machine Learning for Precision Psychiatry](https://mlsanity.com/paper/2204.01607/) (2022)
- [Deep Bag-of-Sub-Emotions for Depression Detection in Social Media](https://mlsanity.com/paper/2103.01334/) (2021)
- [Towards Explainable and Safe Conversational Agents for Mental Health: A Survey](https://mlsanity.com/paper/2304.13191/) (2023)
- [Deep Knowledge-Infusion For Explainable Depression Detection](https://mlsanity.com/paper/2409.02122/) (2024)
- [STEP-EZ: Syntax Tree guided semantic ExPlanation for Explainable Zero-shot modeling of clinical depression symptoms from text](https://mlsanity.com/paper/2106.10928/) (2021)
- [Depression Symptoms Modelling from Social Media Text: A Semi-supervised Learning Approach](https://mlsanity.com/paper/2209.02765/) (2022)
- [Heterogeneous Subgraph Network with Prompt Learning for Interpretable Depression Detection on Social Media](https://mlsanity.com/paper/2407.09019/) (2024)
- [They Look Like Each Other: Case-based Reasoning for Explainable Depression Detection on Twitter using Large Language Models](https://mlsanity.com/paper/2407.21041/) (2024)
- [Decoding Linguistic Nuances in Mental Health Text Classification Using Expressive Narrative Stories](https://mlsanity.com/paper/2412.16302/) (2024)
- [LLM Assistance for Pediatric Depression](https://mlsanity.com/paper/2501.17510/) (2025)
- [Social Behavior and Mental Health: A Snapshot Survey under COVID-19 Pandemic](https://mlsanity.com/paper/2105.08165/) (2021)
- [Deep Temporal Modelling of Clinical Depression through Social Media Text](https://mlsanity.com/paper/2211.07717/) (2022)
- [Cordyceps@LT-EDI: Depression Detection with Reddit and Self-training](https://mlsanity.com/paper/2310.01418/) (2023)
- [Evaluating Lexicon Incorporation for Depression Symptom Estimation](https://mlsanity.com/paper/2404.19359/) (2024)
- [Integrating Emotion Distribution Networks and Textual Message Analysis for X User Emotional State Classification](https://mlsanity.com/paper/2504.10521/) (2025)

## Abstract (arXiv)

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.