Openly accessible

The relationship between linguistic expression in blog content and symptoms of depression, anxiety, and suicidal thoughts: a longitudinal study

O'Dea, Bridianne, Boonstra, Tjeerd W., Larsen, Mark E., Nguyen, Thin, Venkatesh, Svetha and Christensen, Helen 2021, The relationship between linguistic expression in blog content and symptoms of depression, anxiety, and suicidal thoughts: a longitudinal study, PLoS One, vol. 16, no. 5, pp. 1-17, doi: 10.1371/journal.pone.0251787.

Attached Files
Name Description MIMEType Size Downloads

Title The relationship between linguistic expression in blog content and symptoms of depression, anxiety, and suicidal thoughts: a longitudinal study
Author(s) O'Dea, Bridianne
Boonstra, Tjeerd W.
Larsen, Mark E.
Nguyen, ThinORCID iD for Nguyen, Thin orcid.org/0000-0003-3467-8963
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Christensen, Helen
Journal name PLoS One
Volume number 16
Issue number 5
Article ID e0251787
Start page 1
End page 17
Total pages 17
Publisher Public Library of Science
Place of publication San Francisco, Calif.
Publication date 2021-05
ISSN 1932-6203
1932-6203
Keyword(s) DISORDER
ILLNESS
MENTAL-HEALTH PROBLEMS
Multidisciplinary Sciences
Science & Technology
Science & Technology - Other Topics
SOCIAL MEDIA
TWITTER
Summary Data generated within social media platforms may present a new way to identify individuals who are experiencing mental illness. This study aimed to investigate the associations between linguistic features in individuals’ blog data and their symptoms of depression, generalised anxiety, and suicidal ideation. Individuals who blogged were invited to participate in a longitudinal study in which they completed fortnightly symptom scales for depression and anxiety (PHQ-9, GAD-7) for a period of 36 weeks. Blog data published in the same period was also collected, and linguistic features were analysed using the LIWC tool. Bivariate and multivariate analyses were performed to investigate the correlations between the linguistic features and symptoms between subjects. Multivariate regression models were used to predict longitudinal changes in symptoms within subjects. A total of 153 participants consented to the study. The final sample consisted of the 38 participants who completed the required number of symptom scales and generated blog data during the study period. Between-subject analysis revealed that the linguistic features “tentativeness” and “non-fluencies” were significantly correlated with symptoms of depression and anxiety, but not suicidal thoughts. Within-subject analysis showed no robust correlations between linguistic features and changes in symptoms. The findings may provide evidence of a relationship between some linguistic features in social media data and mental health; however, the study was limited by missing data and other important considerations. The findings also suggest that linguistic features observed at the group level may not generalise to, or be useful for, detecting individual symptom change over time.
Language eng
DOI 10.1371/journal.pone.0251787
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30151864

Document type: Journal Article
Collections: Open Access Collection
A2I2 (Applied Artificial Intelligence Institute)
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 19 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Tue, 13 Jul 2021, 11:02:07 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.