Abstract
Since more and more users participate in various social networking services, the volume of streaming data is considerably increasing. It is necessary to find out valuable messages from huge data archived every moment. This paper investigates the problem of detecting informative messages in Twitter, and proposes effective methods to solve the problem based on User History. Most of the sheer information in tweets has a common defect which is the fact that it is affected by influence of User level within the Twitter network. Our key idea is to leverage each user's history observed from a large scale dataset as features to determine whether a new message is informative or not, compared to their previous messages. This allows us to normalize influence of individual user on tweets and to estimate the probability of informativeness. Experimental results on a real Twitter data show that our method can effectively improve the performance on identifying informative tweets.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 162-173 |
| Number of pages | 12 |
| Volume | 7675 LNCS |
| DOIs | |
| Publication status | Published - 2012 Dec 31 |
| Event | 8th Asia Information Retrieval Societies Conference, AIRS 2012 - Tianjin, China Duration: 2012 Dec 17 → 2012 Dec 19 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 7675 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 8th Asia Information Retrieval Societies Conference, AIRS 2012 |
|---|---|
| Country | China |
| City | Tianjin |
| Period | 12/12/17 → 12/12/19 |
Fingerprint
Keywords
- Information filtering
- Recommendation system
- Social media
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Detecting informative messages based on user history in twitter. / Chun, Chang Woo; Lee, Jung Tae; Lee, Seung Wook; Rim, Hae-Chang.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7675 LNCS 2012. p. 162-173 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7675 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Detecting informative messages based on user history in twitter
AU - Chun, Chang Woo
AU - Lee, Jung Tae
AU - Lee, Seung Wook
AU - Rim, Hae-Chang
PY - 2012/12/31
Y1 - 2012/12/31
N2 - Since more and more users participate in various social networking services, the volume of streaming data is considerably increasing. It is necessary to find out valuable messages from huge data archived every moment. This paper investigates the problem of detecting informative messages in Twitter, and proposes effective methods to solve the problem based on User History. Most of the sheer information in tweets has a common defect which is the fact that it is affected by influence of User level within the Twitter network. Our key idea is to leverage each user's history observed from a large scale dataset as features to determine whether a new message is informative or not, compared to their previous messages. This allows us to normalize influence of individual user on tweets and to estimate the probability of informativeness. Experimental results on a real Twitter data show that our method can effectively improve the performance on identifying informative tweets.
AB - Since more and more users participate in various social networking services, the volume of streaming data is considerably increasing. It is necessary to find out valuable messages from huge data archived every moment. This paper investigates the problem of detecting informative messages in Twitter, and proposes effective methods to solve the problem based on User History. Most of the sheer information in tweets has a common defect which is the fact that it is affected by influence of User level within the Twitter network. Our key idea is to leverage each user's history observed from a large scale dataset as features to determine whether a new message is informative or not, compared to their previous messages. This allows us to normalize influence of individual user on tweets and to estimate the probability of informativeness. Experimental results on a real Twitter data show that our method can effectively improve the performance on identifying informative tweets.
KW - Information filtering
KW - Recommendation system
KW - Social media
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=84871576605&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84871576605&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-35341-3_14
DO - 10.1007/978-3-642-35341-3_14
M3 - Conference contribution
AN - SCOPUS:84871576605
SN - 9783642353406
VL - 7675 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 162
EP - 173
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -