# Forecasting the onset and course of mental illness with Twitter data

arXiv: https://arxiv.org/abs/1608.07740 · HTML: https://mlsanity.com/paper/1608.07740/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2016.

## Built on by (most cited first, 25 of 25)

- [Data set creation and empirical analysis for detecting signs of depression from social media postings](https://mlsanity.com/paper/2202.03047/) (2022)
- [Characterizing Delusional Spirals through Human-LLM Chat Logs](https://mlsanity.com/paper/2603.16567/) (2026)
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- [Prediction of Depression Severity Based on the Prosodic and Semantic Features with Bidirectional LSTM and Time Distributed CNN](https://mlsanity.com/paper/2202.12456/) (2022)
- [Detection of financial opportunities in micro-blogging data with a stacked classification system](https://mlsanity.com/paper/2404.07224/) (2024)
- [Robust language-based mental health assessments in time and space through social media](https://mlsanity.com/paper/2302.12952/) (2023)
- [Deep Bag-of-Sub-Emotions for Depression Detection in Social Media](https://mlsanity.com/paper/2103.01334/) (2021)
- [Analysis of Twitter Users' Lifestyle Choices using Joint Embedding Model](https://mlsanity.com/paper/2104.03189/) (2021)
- [Twitter User Representation Using Weakly Supervised Graph Embedding](https://mlsanity.com/paper/2108.08988/) (2021)
- [Then and Now: Quantifying the Longitudinal Validity of Self-Disclosed Depression Diagnoses](https://mlsanity.com/paper/2206.11155/) (2022)
- [Depression Detection Using Digital Traces on Social Media: A Knowledge-aware Deep Learning Approach](https://mlsanity.com/paper/2303.05389/) (2023)
- [Quantifying language changes surrounding mental health on Twitter](https://mlsanity.com/paper/2106.01481/) (2021)
- [Sentiment and structure in word co-occurrence networks on Twitter](https://mlsanity.com/paper/2110.00587/) (2021)
- [Mental Disorder Classification via Temporal Representation of Text](https://mlsanity.com/paper/2406.15470/) (2024)
- [The Language of Trauma: Modeling Traumatic Event Descriptions Across Domains with Explainable AI](https://mlsanity.com/paper/2408.05977/) (2024)
- [Mental Disorders on Online Social Media Through the Lens of Language and Behaviour: Analysis and Visualisation](https://mlsanity.com/paper/2202.03291/) (2022)
- [Depression Symptoms Modelling from Social Media Text: A Semi-supervised Learning Approach](https://mlsanity.com/paper/2209.02765/) (2022)
- [The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions](https://mlsanity.com/paper/2501.11714/) (2025)
- [Social Behavior and Mental Health: A Snapshot Survey under COVID-19 Pandemic](https://mlsanity.com/paper/2105.08165/) (2021)
- [A comprehensive empirical analysis on cross-domain semantic enrichment for detection of depressive language](https://mlsanity.com/paper/2106.12797/) (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)
- [A Review: PTSD in Pre-Existing Medical Condition on Social Media](https://mlsanity.com/paper/2601.08836/) (2025)
- [A Formative Study of Brief Affective Text as a Complement to Wearable Sensing for Longitudinal Student Health Monitoring](https://mlsanity.com/paper/2605.14360/) (2026)
- [VIS-DICT: A Visual Dictionary for Missing Modality Imputation in Social Network Depression Detection](https://mlsanity.com/paper/2609.05537/) (2026)

## Abstract (arXiv)

We developed computational models to predict the emergence of depression and Post-Traumatic Stress Disorder in Twitter users. Twitter data and details of depression history were collected from 204 individuals (105 depressed, 99 healthy). We extracted predictive features measuring affect, linguistic style, and context from participant tweets (N=279,951) and built models using these features with supervised learning algorithms. Resulting models successfully discriminated between depressed and healthy content, and compared favorably to general practitioners' average success rates in diagnosing depression. Results held even when the analysis was restricted to content posted before first depression diagnosis. State-space temporal analysis suggests that onset of depression may be detectable from Twitter data several months prior to diagnosis. Predictive results were replicated with a separate sample of individuals diagnosed with PTSD (174 users, 243,775 tweets). A state-space time series model revealed indicators of PTSD almost immediately post-trauma, often many months prior to clinical diagnosis. These methods suggest a data-driven, predictive approach for early screening and detection of mental illness.