TY  - JOUR
PY  - 2013//
TI  - Two applications of clustering techniques to Twitter: Community detection and issue extraction
JO  - Discrete dynamics in nature and society
A1  - Kim, Yong-Hyuk
A1  - Seo, Sehoon
A1  - Ha, Yong-Ho
A1  - Lim, Seongwon
A1  - Yoon, Yourim
SP  - 1
EP  - 8
VL  - 2013
IS  - 
N2  - Twitter’s recent growth in the number of users has redefined its status from a simple social media service to a mass media. We deal with clustering techniques applied to Twitter network and Twitter trend analysis. When we divide and cluster Twitter network, we can find a group of users with similar inclination, called a “Community.” In this regard, we introduce the Louvain algorithm and advance a partitioned Louvain algorithm as its improved variant. In the result of the experiment based on actual Twitter data, the partitioned Louvain algorithm supplemented the performance decline and shortened the execution time. Also, we use clustering techniques for trend analysis. We use nonnegative matrix factorization (NMF), which is a convenient method to intuitively interpret and extract issues on various time scales. By cross-verifying the results using NFM, we found that it has clear correlation with the actual main issue.<p />  <p>Language: en</p>
LA  - en
SN  - 1026-0226
UR  - http://dx.doi.org/10.1155/2013/903765
ID  - ref1
ER  -