Multimodal depression detection on instagram considering time interval of posts

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)25-47
Number of pages23
JournalJournal of Intelligent Information Systems
Issue number1
Publication statusPublished - 2021 Feb


  • Deep learning
  • Depression detection
  • Social media

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence


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