APSIPA Transactions on Signal and Information Processing > Vol 10 > Issue 1

Social rhythms measured via social media use for predicting psychiatric symptoms

Kenji Yokotani, Tokushima University, Japan, yokotanikenji@tokushima-u.ac.jp , Masanori Takano, CyberAgent, Inc., Japan
 
Suggested Citation
Kenji Yokotani and Masanori Takano (2021), "Social rhythms measured via social media use for predicting psychiatric symptoms", APSIPA Transactions on Signal and Information Processing: Vol. 10: No. 1, e16. http://dx.doi.org/10.1017/ATSIP.2021.17

Publication Date: 28 Oct 2021
© 2021 Kenji Yokotani and Masanori Takano
 
Subjects
 
Keywords
Fast Chirplet transformperceived emotional social supportpsychiatric symptomssocial mediasocial rhythms
 

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This is published under the terms of the Creative Commons Attribution licence.

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In this article:
I. INTRODUCTION 
II. METHODS 
III. RESULTS 
IV. DISCUSSION 
V. CONCLUSION 

Abstract

Social rhythms have been considered as relevant to mood disorders, but detailed analysis of social rhythms has been limited. Hence, we aim to assess social rhythms via social media use and predict users' psychiatric symptoms through their social rhythms. A two-wave survey was conducted in the Pigg Party, a popular Japanese avatar application. First and second waves of data were collected from 3504 and 658 Pigg Party users, respectively. The time stamps of their communication were sampled. Furthermore, the participants answered the General Health Questionnaire and perceived emotional support in the Pigg Party. The results indicated that social rhythms of users with many social supports were stable in a 24-h cycle. However, the rhythms of users with few social supports were disrupted. To predict psychiatric symptoms via social rhythms in the second-wave data, the first-wave data were used for training. We determined that fast Chirplet transformation was the optimal transformation for social rhythms, and the best accuracy scores on psychiatric symptoms and perceived emotional support in the second-wave data corresponded to 0.9231 and 0.7462, respectively. Hence, measurement of social rhythms via social media use enabled detailed understanding of emotional disturbance from the perspective of time-varying frequencies.

DOI:10.1017/ATSIP.2021.17