Name
Abstract | ||
|---|---|---|
Depression has become a public health concern around the world. Traditional methods for detecting depression rely on self-report techniques, which suffer from inefficient data collection and processing. This paper built both classification and regression models based on linguistic and behavioral features acquired from 10,102 social media users, and compared classification and prediction accuracy respectively among models built on different observation windows. Results showed that users' depression can be predicted via social media. The best result appears when we make prediction in advance for half a month with a 2-month length of observation time. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/WI-IAT.2015.166 | WI-IAT |
Keywords | Field | DocType |
Machine learning, Depression, Microblogging behavior, Classification, Prediction | Data mining,Data collection,Pragmatics,Social media,Information retrieval,Computer science,Regression analysis,Feature extraction,Correlation,Artificial intelligence,Machine learning | Conference |
Volume | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
2 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Quan Hu | 1 | 0 | 0.34 |
| Ang Li | 2 | 42 | 5.37 |
| Fei Heng | 3 | 0 | 0.34 |
| Jianpeng Li | 4 | 0 | 0.34 |
| Tingshao Zhu | 5 | 192 | 33.61 |