Avatars' Social Rhythms in Online Games Indicate Their Players' Depression

Bibliographic Data

ID22004178
AuthorsKenji Yokotani (0000-0002-9711-157X, Tokushima University, corresponding author), Masanori Takano (0000-0002-4354-0619, CyberAgent (Japan))
Year2022
Volume25
Issue11
Pages718-732
Publication date2022-11-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueCyberpsychology Behavior and Social Networking (JOURNAL)
Journal identifiersISSN: 2152-2715 • E-ISSN: 2152-2723
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1089/cyber.2022.0058
PMID36282796
OpenAlexW4307218215
LanguageEN
Citations received1
References cited40

Data sets on gameplay, called digital biomarkers, contain many characteristics of game players and are associated with mental health problems. In fact, an avatar's behavior during an online game is said to be related to its player's mental health. Based on this information, we estimated the depressive state of players based on their avatar's behavioral logs. There were 3,361 participants who were players of Pigg Party, a popular Japanese online game application. In April 2020, the players logged into the Pigg Party and answered a questionnaire on depression. In May 2020, of the 3,361 participants, 658 players again logged into the Pigg Party and answered the depression questionnaire. Their responses to the questionnaire and behavioral logs of Pigg Party for April and May comprised the data sets used in the study. The data set from April showed that individuals without depression were more likely to perform activities in a 24-hour cycle than individuals with depression. Furthermore, the popular model of the time series data set trained on the April data set predicted depression in the May data set with an accuracy of around 0.3 to 0.4. The results suggest that these models are difficult to predict for sparse time series data sets, such as the study's data set, and require refinement

Avatar · Data set · Human–computer interaction · Mental health · Psychiatry · Applied Psychology · Computer Science · Digital Mental Health Interventions · Mental Health Research Topics · Psychological and Temporal Perspectives Research · Psychology · Artificial Intelligence

Tools

Open DOI