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
Y1 - 2012
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
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 162
EP - 173
BT - Information Retrieval Technology - 8th Asia Information Retrieval Societies Conference, AIRS 2012, Proceedings
T2 - 8th Asia Information Retrieval Societies Conference, AIRS 2012
Y2 - 17 December 2012 through 19 December 2012
ER -