@inproceedings{alqahtani-etal-2022-quantitative,
title = "A Quantitative and Qualitative Analysis of Schizophrenia Language",
author = "Alqahtani, Amal and
Kayi, Efsun Sarioglu and
Hamidian, Sardar and
Compton, Michael and
Diab, Mona",
editor = "Lavelli, Alberto and
Holderness, Eben and
Jimeno Yepes, Antonio and
Minard, Anne-Lyse and
Pustejovsky, James and
Rinaldi, Fabio",
booktitle = "Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.louhi-1.20/",
doi = "10.18653/v1/2022.louhi-1.20",
pages = "173--183",
abstract = "Schizophrenia is one of the most disabling mental health conditions to live with. Approximately one percent of the population has schizophrenia which makes it fairly common, and it affects many people and their families. Patients with schizophrenia suffer different symptoms: formal thought disorder (FTD), delusions, and emotional flatness. In this paper, we quantitatively and qualitatively analyze the language of patients with schizophrenia measuring various linguistic features in two modalities: speech and written text. We examine the following features: coherence and cohesion of thoughts, emotions, specificity, level of commit- ted belief (LCB), and personality traits. Our results show that patients with schizophrenia score high in fear and neuroticism compared to healthy controls. In addition, they are more committed to their beliefs, and their writing lacks details. They score lower in most of the linguistic features of cohesion with significant p-values."
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<abstract>Schizophrenia is one of the most disabling mental health conditions to live with. Approximately one percent of the population has schizophrenia which makes it fairly common, and it affects many people and their families. Patients with schizophrenia suffer different symptoms: formal thought disorder (FTD), delusions, and emotional flatness. In this paper, we quantitatively and qualitatively analyze the language of patients with schizophrenia measuring various linguistic features in two modalities: speech and written text. We examine the following features: coherence and cohesion of thoughts, emotions, specificity, level of commit- ted belief (LCB), and personality traits. Our results show that patients with schizophrenia score high in fear and neuroticism compared to healthy controls. In addition, they are more committed to their beliefs, and their writing lacks details. They score lower in most of the linguistic features of cohesion with significant p-values.</abstract>
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%0 Conference Proceedings
%T A Quantitative and Qualitative Analysis of Schizophrenia Language
%A Alqahtani, Amal
%A Kayi, Efsun Sarioglu
%A Hamidian, Sardar
%A Compton, Michael
%A Diab, Mona
%Y Lavelli, Alberto
%Y Holderness, Eben
%Y Jimeno Yepes, Antonio
%Y Minard, Anne-Lyse
%Y Pustejovsky, James
%Y Rinaldi, Fabio
%S Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F alqahtani-etal-2022-quantitative
%X Schizophrenia is one of the most disabling mental health conditions to live with. Approximately one percent of the population has schizophrenia which makes it fairly common, and it affects many people and their families. Patients with schizophrenia suffer different symptoms: formal thought disorder (FTD), delusions, and emotional flatness. In this paper, we quantitatively and qualitatively analyze the language of patients with schizophrenia measuring various linguistic features in two modalities: speech and written text. We examine the following features: coherence and cohesion of thoughts, emotions, specificity, level of commit- ted belief (LCB), and personality traits. Our results show that patients with schizophrenia score high in fear and neuroticism compared to healthy controls. In addition, they are more committed to their beliefs, and their writing lacks details. They score lower in most of the linguistic features of cohesion with significant p-values.
%R 10.18653/v1/2022.louhi-1.20
%U https://aclanthology.org/2022.louhi-1.20/
%U https://doi.org/10.18653/v1/2022.louhi-1.20
%P 173-183
Markdown (Informal)
[A Quantitative and Qualitative Analysis of Schizophrenia Language](https://aclanthology.org/2022.louhi-1.20/) (Alqahtani et al., Louhi 2022)
ACL
- Amal Alqahtani, Efsun Sarioglu Kayi, Sardar Hamidian, Michael Compton, and Mona Diab. 2022. A Quantitative and Qualitative Analysis of Schizophrenia Language. In Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI), pages 173–183, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.