• Detecting psychosis via natura...

Treffer: Detecting psychosis via natural language processing of social media posts: potentials and pitfalls.

Title:
Detecting psychosis via natural language processing of social media posts: potentials and pitfalls.
Authors:
Plank L; Department of Behavioral and Clinical Neuroscience, Ruhr-University Bochum (RUB), D-44787, Bochum, Germany. Electronic address: laurin.plank@ruhr-uni-bochum.de., Zlomuzica A; Department of Behavioral and Clinical Neuroscience, Ruhr-University Bochum (RUB), D-44787, Bochum, Germany.
Source:
Neuropsychologia [Neuropsychologia] 2026 Jan 30; Vol. 221, pp. 109325. Date of Electronic Publication: 2025 Nov 20.
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Pergamon Press Country of Publication: England NLM ID: 0020713 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-3514 (Electronic) Linking ISSN: 00283932 NLM ISO Abbreviation: Neuropsychologia Subsets: MEDLINE
Imprint Name(s):
Publication: Oxford : Pergamon Press
Original Publication: Oxford.
MeSH Terms:
Psychotic Disorders*/diagnosis , Natural Language Processing* , Social Media*, Humans ; Speech
Contributed Indexing:
Keywords: Formal thought disorder; Mental disorder; Natural language processing; Psychosis; Social media
Entry Date(s):
Date Created: 20251122 Date Completed: 20260112 Latest Revision: 20260114
Update Code:
20260115
DOI:
10.1016/j.neuropsychologia.2025.109325
PMID:
41274635
Database:
MEDLINE

Weitere Informationen

The early detection and continuous monitoring of psychosis is of utmost importance in ensuring timely and effective treatment. Current mental health care is unable to meet this demand, partially because methods to detect psychosis are relatively time-intensive and not scalable to large populations. Consequently, there has been an increasing focus on the potential of passive data collection from digital devices to overcome this issue. In the present article, we explore whether the analysis of social media (SM) posts through natural language processing (NLP) could improve the detection of psychosis. We first demonstrate how freely expressed speech can be processed automatically in the laboratory to predict and classify psychosis with high levels of accuracy. We further outline the current state of psychosis classification from SM-derived data and discuss methodological issues that are hampering progress in this field. Finally, we delve into potential pitfalls of such systems and provide insight into how these may be circumvented.
(Copyright © 2025 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Declaration of competing interest The authors declare that they have no competing interests.