TY - GEN
T1 - Early identification of personalized trending topics in microblogging
AU - Wu, Liang
AU - Hu, Xia
AU - Liu, Huan
N1 - Funding Information:
The work is funded, in part, by ONR N00014-16-1-2257 and the Department of Defense under the MINERVA initiative through the ONR N000141310835.
PY - 2017
Y1 - 2017
N2 - Social media has become a primary platform for the spread of information. Trending topics, which are breaking news and immediately popular stories, have become an attractive data source facilitating the spread of emerging issues. Motivated by the diverse trending topics covering from sports to politics, it is essential to help users find personalized trending topics. Since a topic in social media may start trending and get obsoleted quickly, the personalization would be more valuable to a user if the trending topic can be recommended before it is outdated. In order to identify personalized trending topics at an early stage, we propose to identify and exploit the auxiliary information. In particular, through collectively modeling content of similar users with social network information, we identify additional past contents that can enrich the training data of trending topics and users. The key insight is that though most posts of a user may be irrelevant, a few key posts can be signals revealing interests towards a particular topic. Experiments on real-world data demonstrate that our proposed approach effectively personalizes trending topics when they just start trending.
AB - Social media has become a primary platform for the spread of information. Trending topics, which are breaking news and immediately popular stories, have become an attractive data source facilitating the spread of emerging issues. Motivated by the diverse trending topics covering from sports to politics, it is essential to help users find personalized trending topics. Since a topic in social media may start trending and get obsoleted quickly, the personalization would be more valuable to a user if the trending topic can be recommended before it is outdated. In order to identify personalized trending topics at an early stage, we propose to identify and exploit the auxiliary information. In particular, through collectively modeling content of similar users with social network information, we identify additional past contents that can enrich the training data of trending topics and users. The key insight is that though most posts of a user may be irrelevant, a few key posts can be signals revealing interests towards a particular topic. Experiments on real-world data demonstrate that our proposed approach effectively personalizes trending topics when they just start trending.
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M3 - Conference contribution
AN - SCOPUS:85029428563
T3 - Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017
SP - 692
EP - 695
BT - Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017
PB - AAAI press
T2 - 11th International Conference on Web and Social Media, ICWSM 2017
Y2 - 15 May 2017 through 18 May 2017
ER -