# Predictive Linguistic Features of Schizophrenia

arXiv: https://arxiv.org/abs/1810.09377 · HTML: https://mlsanity.com/paper/1810.09377/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2018.

## Built on by (most cited first, 5 of 5)

- [Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts](https://mlsanity.com/paper/2307.02768/) (2023)
- [A Simple and Flexible Modeling for Mental Disorder Detection by Learning from Clinical Questionnaires](https://mlsanity.com/paper/2306.02955/) (2023)
- [A Comprehensive Review of Datasets for Clinical Mental Health AI Systems](https://mlsanity.com/paper/2508.09809/) (2025)
- [Leveraging Machine-Generated Rationales to Facilitate Social Meaning Detection in Conversations](https://mlsanity.com/paper/2406.19545/) (2024)
- [A Quantitative and Qualitative Analysis of Schizophrenia Language](https://mlsanity.com/paper/2201.10430/) (2022)

## Abstract (arXiv)

Schizophrenia is one of the most disabling and difficult to treat of all human medical/health conditions, ranking in the top ten causes of disability worldwide. It has been a puzzle in part due to difficulty in identifying its basic, fundamental components. Several studies have shown that some manifestations of schizophrenia (e.g., the negative symptoms that include blunting of speech prosody, as well as the disorganization symptoms that lead to disordered language) can be understood from the perspective of linguistics. However, schizophrenia research has not kept pace with technologies in computational linguistics, especially in semantics and pragmatics. As such, we examine the writings of schizophrenia patients analyzing their syntax, semantics and pragmatics. In addition, we analyze tweets of (self pro-claimed) schizophrenia patients who publicly discuss their diagnoses. For writing samples dataset, syntactic features are found to be the most successful in classification whereas for the less structured Twitter dataset, a combination of features performed the best.