Name
Abstract | ||
|---|---|---|
Over the last few years, Twitter is increasingly becoming an important source of up-to-date topics about what is happening in the world. In this paper, we propose a dynamic topic association detection model to discover relations between Twitter topics, by which users can gain insights into richer information about topics of interest. The proposed model utilizes a time constrained method to extract event-based spatio-temporal topic association, and constructs a dynamic temporal map to represent the obtained result. Experimental results show the improvement of the proposed model compared to static spatio-temporal method and co-occurrence method. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2187980.2188149 | WWW (Companion Volume) |
Keywords | Field | DocType |
important source,dynamic temporal map,dynamic topic association detection,spatio-temporal topic association,static spatio-temporal method,up-to-date topic,co-occurrence method,twitter topic,dynamic association | Data science,Data mining,World Wide Web,Computer science,Constrained method | Conference |
Citations | PageRank | References |
12 | 0.78 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuangyong Song | 1 | 72 | 4.34 |
| Qiudan Li | 2 | 440 | 28.06 |
| Hongyun Bao | 3 | 48 | 4.23 |