Name
Abstract | ||
|---|---|---|
Social Anxiety disorder (SAD), one of the most important and common mental disorders, affects a large proportion of individuals around the world. To help identify SAD patients for early intervention, in this study, we bring together computer science and psychology for a new research problem, the automatic identification of SAD patients with online social network data. We extract multiple effective features with the data from 200 online social network users. The effectiveness indicates that our system is promising in identifying SAD patients with online social network data. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/MDM48529.2020.00073 | 2020 21st IEEE International Conference on Mobile Data Management (MDM) |
Keywords | DocType | ISSN |
social anxiety disorder,mental disorder detection,machine learning | Conference | 1551-6245 |
ISBN | Citations | PageRank |
978-1-7281-4664-5 | 0 | 0.34 |
References | Authors | |
12 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming-Yi Chang | 1 | 4 | 1.87 |
| Chih-Ying Tseng | 2 | 0 | 0.34 |