Name
Abstract | ||
|---|---|---|
The rapid development of social media brings about vast user generated content. Computational cyber-psychology, an interdisciplinary subject area, employs machine learning approaches to explore underlying psychological patterns. Our research aims at identifying users' mental health status through their social media behavior. We collected both users' social media data and mental health data from the most popular Chinses microblog service provider, Sina Weibo. By extracting linguistic and behavior features, and applying machine learning algorithms, we made preliminary exploration to identify users' mental health status automaticly, which previously is mainly measured by well-designed psychological questionnaire. Our classification model achieves the accuracy of 72%, and the continous predicting model achieved correlation of 0.3 with questionnaire based score. © 2013 Springer-Verlag Berlin Heidelberg. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-39137-8-12 | HCI (21) |
Keywords | Field | DocType |
automation,mental health,microblog,prediction | User-generated content,World Wide Web,Social media,Computer science,Microblogging,Service provider,Mental health,Multimedia | Conference |
Volume | Issue | ISSN |
8024 LNCS | PART 2 | 16113349 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bibo Hao | 1 | 57 | 6.56 |
| Lin Li | 2 | 20 | 1.74 |
| Ang Li | 3 | 42 | 5.37 |
| Tingshao Zhu | 4 | 192 | 33.61 |