Multimodal depression detection on instagram considering time interval of posts

Chun Yueh Chiu, Hsien Yuan Lane, Jia Ling Koh, Arbee L.P. Chen*

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

1 引文 斯高帕斯(Scopus)

摘要

Depression is a common and serious mental disorder that causes a person to have sad or hopeless feelings in his/her daily life. With the rapid development of social media, people tend to express their thoughts or emotions on the social platform. Different social platforms have various formats of data presentation, which makes huge and diverse data available for analysis by researchers. In our study, we aim to detect users with depressive tendency on Instagram. We create a depression dictionary for automatically collecting data of depressive and non-depressive users. In terms of the prediction model, we construct a multimodal system, which utilizes image, text and behavior features to predict the aggregated depression score of each post on Instagram. Considering the time interval between posts, we propose a two-stage detection mechanism for detecting depressive users. Experimental results demonstrate that our proposed methods can achieve up to 0.835 F1-score for detecting depressive users. It can therefore serve as an early depression detector for a timely treatment before it becomes severe.

原文英語
頁(從 - 到)25-47
頁數23
期刊Journal of Intelligent Information Systems
56
發行號1
DOIs
出版狀態接受/付印 - 2020
對外發佈

ASJC Scopus subject areas

  • 軟體
  • 資訊系統
  • 硬體和架構
  • 電腦網路與通信
  • 人工智慧

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