Work

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

Year: 2025

Type: article

Abstract: 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. • Early detection and continuous monitoring of psychosis improves treatment • Analyzing social media posts could enable scalable psychosis monitoring • Current prediction models have limited validity • Practical and ethical challenges are discussed

Source: Neuropsychologia

DOI: 10.1016/j.neuropsychologia.2025.109325

Authors: Laurin Plank, Armin Zlomuzica

Institution: Ruhr University Bochum

Language: English

Topic: Mental Health via Writing

Subfield: Social Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,300