Name
Abstract | ||
|---|---|---|
With the rapid development of Web 2.0, micro-blogging such as twitter is increasingly becoming an important source of up-to-date topics about what is happening in the world. By analyzing topic trends sequences and identifying relations among topics we have opportunities to gain insights into topic associations and thereby provide better services for micro-bloggers. This paper proposes a novel framework that mines the associations among topic trends in twitter by considering both temporal and location information. The framework consists of the extraction of topics' spatio-temporal information and the calculation of the similarity among topics. The experimental results show that our method can find the related topics effectively and accurately. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15470-6_8 | AMT |
Keywords | Field | DocType |
related topic,related topic search,location information,topic trend,better service,topic trends sequence,novel framework,spatio-temporal framework,up-to-date topic,topic association,spatio-temporal information | Data science,Data mining,World Wide Web,Social media,Query expansion,Computer science,Microblogging | Conference |
Volume | ISSN | ISBN |
6335.0 | 0302-9743 | 3-642-15469-7 |
Citations | PageRank | References |
11 | 0.65 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuangyong Song | 1 | 72 | 4.34 |
| Qiudan Li | 2 | 440 | 28.06 |
| Nan Zheng | 3 | 49 | 3.18 |