Abstract
Millions of posts are being generated in real-time by users in social networking services, such as Twitter. However, a considerable number of those posts are mundane posts that are of interest to the authors and possibly their friends only. This paper investigates the problem of automatically discovering valuable posts that may be of potential interest to a wider audience. Specifically, we model the structure of Twitter as a graph consisting of users and posts as nodes and retweet relations between the nodes as edges. We propose a variant of the HITS algorithm for producing a static ranking of posts. Experimental results on real world data demonstrate that our method can achieve better performance than several baseline methods.
| Original language | English |
|---|---|
| Title of host publication | SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval |
| Pages | 1073-1074 |
| Number of pages | 2 |
| DOIs | |
| Publication status | Published - 2012 Sep 28 |
| Event | 35th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012 - Portland, OR, United States Duration: 2012 Aug 12 → 2012 Aug 16 |
Other
| Other | 35th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012 |
|---|---|
| Country | United States |
| City | Portland, OR |
| Period | 12/8/12 → 12/8/16 |
Keywords
- hits
- social network
- tweet ranking
ASJC Scopus subject areas
- Information Systems
Cite this
Finding interesting posts in Twitter based on retweet graph analysis. / Yang, Min Chul; Lee, Jung Tae; Lee, Seung Wook; Rim, Hae-Chang.
SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. 2012. p. 1073-1074.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Finding interesting posts in Twitter based on retweet graph analysis
AU - Yang, Min Chul
AU - Lee, Jung Tae
AU - Lee, Seung Wook
AU - Rim, Hae-Chang
PY - 2012/9/28
Y1 - 2012/9/28
N2 - Millions of posts are being generated in real-time by users in social networking services, such as Twitter. However, a considerable number of those posts are mundane posts that are of interest to the authors and possibly their friends only. This paper investigates the problem of automatically discovering valuable posts that may be of potential interest to a wider audience. Specifically, we model the structure of Twitter as a graph consisting of users and posts as nodes and retweet relations between the nodes as edges. We propose a variant of the HITS algorithm for producing a static ranking of posts. Experimental results on real world data demonstrate that our method can achieve better performance than several baseline methods.
AB - Millions of posts are being generated in real-time by users in social networking services, such as Twitter. However, a considerable number of those posts are mundane posts that are of interest to the authors and possibly their friends only. This paper investigates the problem of automatically discovering valuable posts that may be of potential interest to a wider audience. Specifically, we model the structure of Twitter as a graph consisting of users and posts as nodes and retweet relations between the nodes as edges. We propose a variant of the HITS algorithm for producing a static ranking of posts. Experimental results on real world data demonstrate that our method can achieve better performance than several baseline methods.
KW - hits
KW - social network
KW - tweet ranking
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=84866603413&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84866603413&partnerID=8YFLogxK
U2 - 10.1145/2348283.2348475
DO - 10.1145/2348283.2348475
M3 - Conference contribution
AN - SCOPUS:84866603413
SN - 9781450316583
SP - 1073
EP - 1074
BT - SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval
ER -