Name
Abstract | ||
|---|---|---|
Mobile phones are quickly becoming the primary source for social and behavioral sensing and data collection. A great deal of research effort in academia and industry is put into mining this data for higher level sense-making, such as understanding user context, inferring social networks, learning individual features, and so on. In this work, we have an attempt to predict a user's status in campus, such as teacher and student. We focus on comparing the difference among voice, message, and stream which we use to predict a user is a teacher or student. Result show that when we use voice, message or stream separately to predict, the results have obvious differences. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CSE.2013.184 | C3S2E |
Keywords | Field | DocType |
obvious difference,higher level sense-making,user context,mobile phone,social network,social networks,social aspects of automation,great deal,research effort,individual feature,behavioral sensing,social sensing,primary source,data mining,data collection,mobile phone users,mobile computing,mobile-phone users | Mobile computing,Data collection,World Wide Web,Social network,Computer science,Computer network,Mobile phone | Conference |
ISSN | Citations | PageRank |
1949-0828 | 4 | 0.43 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Sun | 1 | 4 | 0.43 |
| Jun Zhang | 2 | 408 | 54.35 |
| Lai Tu | 3 | 69 | 8.90 |
| Benxiong Huang | 4 | 168 | 19.36 |