PACIS 2022 Proceedings

Paper Number

1202

Abstract

Depression is a common and severe mental illness. Early detection can reduce costs and improve treatment outcomes. Previous studies mainly relied on the posting behaviors to build automatic detection model for patients with depression but ignored the replying behaviors. This study systematically analyze the replying behavior, identify various features about content, language style and emotion from user-written replies and user-replied posts, and compare their relative importance. The experimental results using real-world dataset reveal that the replying behavior can significantly improve traditional detection model. Compared with posting behavior, replying behavior be shown to be more important for depression detection. Further analysis for the replying behavior shows that the user-written replies is not effective, while user-replied posts are effective. Considering that there are many users who only have replying behaviors, the detection model proposed will be applicable to a larger number of people.

Share

COinS
 

When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.